T1 112012140 Full text

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VOCABULARY PROFILE OF THE NINTH GRADE ENGLISH

TEXTBOOK USED IN SMPN 7 SALATIGA

THESIS

Submitted in Partial Fulfilment of the Requirements for the Degree of

Sarjana Pendidikan

Elis Herdiani Alfian

112012140

ENGLISH LANGUAGE EDUCATION PROGRAM

FACULTY OF LANGUAGE AND LITERATURE

SATYA WACANA CHRISTIAN UNIVERSITY

SALATIGA

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TABLE OF CONTENT

COVER PAGE……… i

APPROVAL PAGE……….ii

COPYRIGHT STATEMENT……….iii

PUBLICATION AGREEMENT DECLARATION... iv

TABLE OF CONTENT………..v

LIST OF TABLE………..vii

ABSTRACT……… 1

INTRODUCTION……….. 1

LITERATURE REVIEW…..………. 3

The Importance of Vocabulary………... 3

How to Successfully Learn and Teach Vocabulary……….... 3

Problems in Learning Vocabulary………. 4

Vocabulary Profiler……… 4

Four Types of Word Frequency………. 5

Relevant Studies Related to the Topic………... 7

THE STUDY……….. 8

Context of the Study………... 8

Material………... 8

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Data Collection Procedure……….. 9

Data Analysis Procedure……….… 9

FINDINGS AND DISCUSSION... 10

A. Overall Vocabulary Profile of the Textbook………..… 10

B. Negative vocabulary profiles of the textbook……….….12

C. Comparison of Vocabulary frequency levels every chapter in the textbook……….. 16

CONCLUSION………..………...… 21

REFERENCES..………...……….………. 23

ACKNOWLEDMENTS...……….……… 24

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LIST OF TABLE

Table 1. K1/the most frequent words (1-1000)………..……. 5

Table 2. K2/less frequent words (1001-2000)………..….. 5

Table 3. Academic vocabulary (AWL words)………..….. 6

Table 4. Low frequency (off list)……….... 6

Table 5.Overall Vocabulary Profile……….. 11

Table 6. Comparison of word frequency levels………....… 17

Table 7.Shared and Unique words in Chapter 5 vs. Chapter 14……….…….…. 19

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INTRODUCTION

Vocabulary is one of main elements to foreign language students’ understanding of what they read in books, what they hear from English teachers in school, and to communicate successfully with other people. In fact, it is important to build up a wide store of English words in a students’ brain since English become a lingua franca in the world. Kweldju and Priyono (2004, as cited in Widiati & Cahyono, 2008) stated that vocabulary is believed to be the most crucial element in a learning process. They also recommend a solution can be found by good handling and by choosing an appropriate vocabulary.

Sometimes English teacher may face difficulties in teaching vocabulary words because they do not notice or find suitable words from textbook to teach students. Students’ lack of vocabulary knowledge may prevent them for understanding textbook being used. With the continuous progress of the technology, there is a tool called vocabulary profiler. Vocabulary profiler is a tool to measure the vocabulary production from a text (Astika, 2014). Therefore, the English teacher can use the tool to describe and calculate the frequency of words used in a textbook and then decide in which groups each word belongs to.

The aim or objective of this research was to analyze vocabulary profile in English course textbook used in SMP N 7 Salatiga entitled ‘‘Think Globally Act Locally’ for grade IX. The book was published in 2014 by Kementrian Pendidikandan Kebudayaan Republik Indonesia.

This research has to be done because hopefully it will help teachers in choosing appropriate vocabulary to teach to the students. Cobb (2010) stated that this tool has been used all over the world to determine word frequency quickly. Furthermore, this tool is trusted, effective, and efficient to use as a benchmark to check book’s qualities. Junior High School was

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chosen since as my experienced teaching PPL ninth grader students in SMPN 7 Salatiga, they find difficulties in learning process used that book, so I wanted to investigate the words frequency of the textbook.

It is important to find out because this research might be helpful for the teachers in Junior High School to know which part of vocabulary is proper for the beginner, intermediate, or advanced students. Also, teachers can teach vocabulary based on the arrangement of the word list whether it is include as academic words, low frequency words, or high frequency words depend on vocabulary profile’s result. However it also important to the writer’ publisher to improve their arrangement of beginner to academic vocabulary in their book became better than before.

Furthermore, the research questions that will be used to address the aim of this study are: 1. What is Vocabulary Profile of The Ninth Grade English Textbook used in SMPN 7 Salatiga? 2. What is the proportion of vocabulary that is not covered in the textbook?

3. What is the token (words) recycling index off the textbook?

LITERATURE REVIEW

The Importance of Vocabulary

Vocabulary is one crucial part in learning processes in class. The statement is supported by McKeown (2002). He stated that vocabulary is one of the important things in language learning, because by the vocabulary knowledge, the learner can build up the language in any language skill. Therefore, Cahyono and Widiati (2008) stated that in vocabulary learning the learner also has to realize concepts of unfamiliar words, achieve as many as words, and use the words successfully for communicate. As Nation (2002) stated, vocabulary development is such

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an important part of language acquisition that have to be monitored and controlled not only in speaking skill and writing skill (productive) but also in listening skill and reading skill (receptive).

How to Successfully Learn and Teach English Vocabulary

In order to make learners successfully learn or teach English vocabulary, teachers have to use appropriate way to teach English in the class. Beck, McKeown, and Kucan (2002) recommended that teachers should choose academic words that appear in a broad variety of texts, provide student-friendly explanations for them, create instructional contexts that supply useful information about new words, and engage students in actively dealing with word meanings using academic words. However, the classification of four type word frequency especially academic words may useful for teacher and students in selecting the vocabulary that is needed and most relevant in learning.

Problems in Learning Vocabulary

The needs of sufficient vocabulary knowledge of the students at their level have been frequently voiced not only by the teachers but also by student teachers who had teaching practicum at participating schools. Students who lack of vocabulary knowledge has to be reduced because it may result students became unable to acquire important language which may affect their English proficiency in class. Moreover, Schmitt (2000) said that students at early stages should learn about 1000-2000 high frequency words and increase about 3000-3000 words families to read authentic text that may include academic words. In order to enrich students vocabulary words, vocabulary profiler can be a guide in determining suitable words to students depend on their level.

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Vocabulary Profiler

By a development of media in English learning there is a tool called vocabulary profiler. Vocabulary profiler itself is an on-line adaptation of Nation and Hwang’s vocabulary assessment instrument (Cobb, 2002). Moreover, Astika (2014) stated that vocabulary profile is a program designed by Laufer and Nation in 1995 which has a function to determine lexical richness (lexical difficulty) of a textbook. This site will be a greatest important especially in learning and teaching English academic vocabulary that focuses on academic vocabulary. Astika (2014) explained that the first step to use the tool is opening the website on www.lextutor.ca/vp, next a text have to be copied and paste into the box program and then the program will analyze and classify where the each word belongs to by viewing the percentages of word group based on four frequency level.

Four Types of Word Frequency

According to Nation (1990) as a long-term exponent of this approach, there are four types of word frequency: high frequency words or known as K1 words (1-1000) and K2 words (1001-2000), academic vocabulary known as AWL words (academic), and the last is low frequency words (off list word).

A high frequency word is the most common words that can be found in any text (Cooper, 2002). Table 1 shows some example of K1/the most frequent words (1-1000) from Cambridge Advanced Learner’s Dictionary:

Table 1.K1/the most frequent words (1-1000)

end are is to

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that the for and

was our as of

Moreover, Table 2 shows some example of K2/less frequent words (1001-2000) from Cambridge Advanced Learner’s Dictionary:

Table 2.K2/less frequent words (1001-2000)

grow ship hardly beauty

easily cities check director

marriage leader rule enemy

factors bridge gas die

Academic vocabulary (AWL words) is worth focusing on for learners wishing to study English language. Table 3 shows some examples of academic words from Cambridge Advance Learner’s Dictionary:

Table 3.Academic vocabulary (AWL words) academy communicate focus positive

accompany context insert respond

benefit economy job style

comment environment locate transport

Low frequency (off list) is a word list that not commonly used in the vocabulary learning strategies (Nobert & Diane, 2012). Table 4 shows some examples of low frequency words from Nation (1990):

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Table 4.Low frequency (off list)

abduct aberration abrasion adventurous

afflict coffle bane bankrupt

barter bespeak caste categorical

chameleon caprice carnage cannibal

Relevant Studies Related To the Topic

There are two relevant studies which also used vocabulary profiler but they had different purposes. The first one is study of Graves (2005) title ‘Vocabulary profile of letters and novels of Jane Austen and her contemporaries’. This study used vocabulary profile to investigate the word used in letters and novels of Jane Austen. Furthermore, as the method he compared the word frequencies of two or more texts by the same author using vocabulary profiler. Then, the result showed there was a close correlation in the frequency of words and three-word sequences in Austen’s novels and letters.

A second study which title ‘The use of vocabulary profiles in predicting the academic and pedagogic performance of TESL trainees’ by Morris and Cobb (2001). This study used vocabulary profiler to investigate the potential of vocabulary profiles as the predictor of academic performance in undergraduate Teaching English as a Second Language (TESL). As the method they analysed 122 TESL students’ writing and scored them whether the result was correlated with the grades they had in their program of study or not using vocabulary profiler. The finding showed that vocabulary result was result the correlation significance with the grades.

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However vocabulary profile was proved became a useful tool in carrying out a standard to make an appropriate assessment of language skill.

The similarity between two researchers above is they used the same tool, vocabulary profiler. Moreover, the difference between them was in the context. Graves’s context was about the vocabulary profile on letters and novels. Moreover, Morris and Cobb’s context was about vocabulary profile on academic performance in undergraduate TESL. However, I used vocabulary profiler to analyse the vocabulary profiles in ninth grade’s book title ‘Think Globally Act Locally’ at SMPN 7 Salatiga since it has not been investigated using vocabulary profiler before.

THE STUDY

The study used descriptive method to study a textbook. According to Rivera and Rivera (2007) descriptive method is used to find facts that appear according on personal judgment or theories which also contains the description, recording, analysis, and interpretation. Therefore, this study used descriptive method to analyze types of vocabularies from a textbook, vocabulary that were not covered, and token word recycling index by using vocabulary profiler.

Context of the Study

The study was conducted at SMP N 7 Salatiga. It is located at Setiaki 15, Salatiga and it is one of public schools in Salatiga. The main reason why the study chose that school as the context of the research was because the textbook has not been profiled using vocabulary profiler. Another reason was since the researcher did the Teaching Practicum (PPL) in SMPN 7 Salatiga, the textbook material for ninth graders was easy to be accessed.

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Material

The study focused on ninth graders book entitled ‘Think Globally Act Locally’ which contains fourteen chapters. All chapters inside the book are used to find out in which group each vocabularies belongs to, vocabulary that were not covered, and token word recycling index. The study used ninth graders’ book as a material because ninth grader soon will do final examination to continue their study to Senior High School. Therefore, they had to prepare more and enrich their vocabulary English to strengthen their preparation before final examination.

Data Collection Instruments

The study used vocabulary profiler as a tool. This tool can be accessed through www.lextutor.ca.vp. One of its functions is to check vocabulary profile used in a textbook. The result can be shown in different striking color; blue for high frequency words, yellow for academic words, and red for low frequency words.

Data Collection Procedure

To collect the data, all words from the e-book materials have to be copied from e-book into Microsoft Word first to make it easier and practical before the words being moved to box in Vocabulary Profiler. Every chapter would be copied in different Microsoft Word file, so that the total was fourteen Microsoft Word files.

Data Analysis Procedure

The result that has been shown by vocabulary profiler would be analysed by grouping the vocabularies into K1 (1-1000), K2 (1001-2000), academic words, or off list words. As a following action the researcher described the table result in a form of paragraph to make the

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result clearer. It also analysed list of vocabulary that were not covered in textbook and the token (word) recycling index of the textbook.

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FINDINGS AND DISCUSSION

This study showed the identification of vocabulary profile of Think Globally Act Locally book for grade IX. Vocabulary profiler was used to calculate and analyze every word in chapter 1 until 14 which total 40.621 words. Furthermore, the finding was separated in three big parts. The first part of this section presented the overall vocabulary profile of the textbook showing the proportions of the vocabulary frequency classifications. The second part showed the negative vocabulary profiles of 1, K-2, and AWL with lists of the vocabulary items that were not found in the textbook, also block frequency output of off-list words. The last part of this section showed the comparison of the chapter in the textbook with regard to the vocabulary items that were contrast. The comparison also showed the token recycling index that provided information about an estimate of chapter comprehensibility.

Here, before the words being analyzed some words were eliminated to reduce the result of off list words such as number, name, to be (am, is, are, was, and were), Indonesian words, name of city, and name of country. Therefore, the spelling mistakes were also corrected.

D. Overall Vocabulary Profile of the Textbook

The analysis began by combining 14 chapters into one then analyzed using vocabulary profiler. The result was presented in Table 5.

31 Table 5. Overall Vocabulary Profile

FREQ. LEVEL FAMILIES % TYPES %

TOKENS %

CUMULATIVE TOKEN%

K-1 WORDS 689

58.24%

1192 47.76%

22770 81.22%

81.22%

K-2 WORDS 368

31.11%

498 19.95%

2492 8.89%

90.11%

AWL [570 FAMS] TOT 2,570

126 10.65%

157 6.29%

563 2.01%

92.12%

OFF-LIST: ?? 655

26.24%

2210 7.88%

100.00%

TOTAL

(UNROUNDED)

1183+? 2496 100%

28035 100%

100.00%

The first row in Table 5 showed three terms; family, type, and token. Word family is head word, for example: the family or head word of maker and making is make. Type is different words, for example: committee and comparison are different words. While discussion and discussing, or discussed are considered as the same type. Token is words in a text; the total number of words in a text. For example, if in a text there are submit [3], coming [1], really [3], and father [2], the number of token is 9.

Table 5 shows that the vocabulary used in the textbook was in most frequently used 1000 words group (K-1). With the additional K-2 word coverage (8.89%) the cumulative percentage of the word coverage was 90.11%, which is relatively close estimate for good comprehension of the texts in the book/assumes that students are familiar with all these words. According to Hirsch (2003), an understanding of 90-95% of the words is necessary for comprehension. Based on the data in Table 5, this word knowledge band should include knowledge of academic words 2.01%. Academic words are those words which are commonly used in academic texts. We need to question whether the academic words that amounted to 563 words used in the textbook need to be introduced to the students in Junior High Schools. Another point that is worth considering is the number of off-list words that reached 2210 words. Although this off-list category is excluded from the frequency list and may occur infrequently

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(low frequency words), it may have words that students at this level need to know. This low frequency list should not be ignored in teaching and teachers have to make a selection for useful words in this category.

E. Negative vocabulary profiles of the textbook

This section presents the description of negative vocabulary profiles of the sample textbook. Negative vocabulary is vocabulary items that are not found in the textbook. These missing words are derived from differences of words in the New General Word List (NGWL) and words in the textbook used in this study. The list of negative K-1 words below is useful for teachers in selecting vocabulary items that students may find useful to develop their vocabulary knowledge.

B.1. Negative VP of K-1

This following analysis shows all the word families (=head words) from the K-1 level that were not found in the textbook. The summary of negative vocabulary profile for K-1 level is presented below: K-1 Total word families: 964

K-1 families in input: 679 (70.44%) K-1 families not in input: 286 (29.67%)

These percentages do not refer to tokens or number of words in the textbook but rather number of word families. According to the summary, 70.44% of word families were found in the textbook, which means, 29.67% of ord fa ilies ere issi g or ot fou d ased o the ords listed in New General Service List (NGSL). The followings are 10% of the word families that are not found in the input textbook. The complete word list has been put in Appendix A ACCOUNT

ACCOUNTABLE ACROSS ACTOR ACTRESS

ADMIT ADVANCE ADVANTAGE

ADVENTURE AFFAIR

AGAINST AGE ALLOW ALONG ANCIENT APPEAR

APPOINT ARISE ARM ARMY ARRIVE ARTICLE

33 ATTACK ATTEMPT

AVERAGE BALL

BAR BATTLE, etc

B.2. Negative VP of K-2

This analysis shows all the word families (=head words) from the K-2 level that were not found in the input textbook. The summary of negative vocabulary profile for K-2 level is presented below: K-2 Total word families: 986

K-2 families in input: 367 (37.22%) K-2 families not in input: 620 (62.88%)

These percentages do not refer to tokens or number of words in the textbook but rather number of word families. According to the summary, 37.22% of word families were found in the textbook, which means, 62.88% of ord fa ilies ere issi g or ot fou d ased o the ords listed in New General Service List (NGSL). The followings are 10% of the word families (K-2) that were not found in the input textbook. The complete word list has been put in Appendix B

ABROAD ABSENT ABSOLUTE ABSOLUTELY ACCUSE ACCUSTOM ADMIRE AEROPLANE AFFORD AGRICULTURE AIM AIRPLANE ALIKE ALIVE ALOUD ALTOGETHER

AMBITION AMUSE ANGLE ANXIETY APART APOLOGIZE APOLOGY APPLAUD APPROVE ARCH ARRANGE ARREST ARROW ASH ASHAMED ASIDE

ASTONISH AUTUMN AVENUE AWAKE AWKWARD AXE BAGGAGE BALANCE BAND BARBER BARE BARELY BARGAIN BARREL BASIN BATHE

BAY BEAK BEAM BEARD BEAST BEAT BEHAVE

BELL BELT BEND BERRY BILLION BIND, and etc

34 B.3. Negative VP of AWL

This analysis shows all the word families (=head words) from the AWL level that were not found in the input textbook. The summary of negative vocabulary profile for AWL level is presented below: AWL Total word families: 569

AWL families in input: 132 (23.20%) AWL families not in input: 438 (76.98%)

These percentages do not refer to tokens or number of words in the textbook but rather number of word families. According to the summary, 23.20% of word families were found in the te t ook, hi h ea s, . % of ord fa ilies ere issi g or ot fou d ased o the ords listed in New General Service List (NGSL). The followings are 10% of the word families that were not found in the textbook. The complete word list has been put in Appendix C.

ABSTRACT ACADEMY ACCESS

ACCOMMODATE

ACCUMULATE ACCURATEACK NOWLEDGE

ACQUIRE ADEQUATE ADJACENT ADMINISTRATE ADVOCATE

AFFECT AGGREGATE AID

ALBEIT ALLOCATE ALTER

ALTERNATIVE AMBIGUOUS AMEND ANALOGY ANNUAL ANTICIPATE

APPARENT APPEND APPRECIATE APPROXIMATE ARBITRARY ASPECT ASSEMBLE ASSESS ASSIGN ASSIST ASSUME ASSURE

ATTACH ATTAIN ATTRIBUTE AUTHOR AUTHORITY AWARE BEHAL

25 B.4. Block frequency output of off-list words.

Description of block frequency output of off-list words in the textbook will be presented here. Using the tool in the Vocabulary Profiler, these words have been frequency-blocked per ten words and have been arranged from high to low frequency. This list of words may provide teachers with useful information as to what words are necessary for teaching. In other words, teachers may select words from the list that are relevant to teaching students at Junior High Schools. Below is the list of Off-list ords, ith 4 tokens and 543 types. Note that RANK is ranking of word, FREQ is frequency of word occurrence, COVERAGE is the percentage of word occurrences, individual or cumulative, and the last column is the vocabulary item. The complete list of blocked frequency has been put in Appendix D.

RANK FREQ COVERAGE

individu cumulative

WORD

1. 66 3.87% 3.87% PUNCTUATION 2. 37 2.17% 6.04% ORALLY 3. 36 2.11% 8.15% BUFFALO 4. 28 1.64% 9.79% ORPHAN 5. 26 1.53% 11.32% INGREDIENTS 6. 23 1.35% 12.67% DRUG

7. 21 1.23% 13.90% ORPHANAGE 8. 20 1.17% 15.07% DIALOGUE 9. 19 1.12% 16.19% MAGAZINE 10. 19 1.12% 17.31% RECIPE 11. 18 1.06% 18.37% BATS

12. 16 0.94% 19.31% HOMEWORK 13. 16 0.94% 20.25% SERA

14. 15 0.88% 21.13% CLASSROOM 15. 15 0.88% 22.01% COCONUT 16. 15 0.88% 22.89% NOVEL 17. 15 0.88% 23.77% UNDERLINE 18. 13 0.76% 24.53% ENCYCLOPEDIA

26 19. 13 0.76% 25.29% FABRICS

20. 13 0.76% 26.05% HANDWRITE

C. Comparison of Vocabulary frequency levels every chapter in the textbook

C.1. Comparison of Vocabulary frequency levels in every chapter

Table 6 compares the frequency of K-1, K-2, AWL, and Off-list among the chapter textbook. It provides a general picture of the extent to which the chapter differed with regards to their frequency groups.

Table 6. Comparison of word frequency levels

Chapters K1 words %

K2 words %

AWL words %

Off list words % Chapter 1 cumulatives % 84.88 84.88% 10.22 95.1% 1.37 96.47% 3.52 100% Chapter 2 cumulatives % 89.1 89.1% 6.55 95.65% 1.27 96.92% 3.08 100% Chapter 3 cumulatives % 85.9 85.9% 8.88 94.78% 0.85 95.63 4.37 100% Chapter 4 cumulatives % 74.42 74.42% 10.44 84.86% 4.65 89.51% 10.49 100% Chapter 5 cumulatives % 69.37 69.37% 16.28 85.65% 3.28 88.93% 11.07 100% Chapter 6 cumulatives % 87.42 87.42% 8.77 96.19% 0.76 96.95% 3.06 100% Chapter 7 cumulatives % 89.29 89.29% 3.97 93.26% 0.86 94.12% 5.87 100% Chapter 8 cumulatives % 88.55 88.55% 6.65 95.2% 1.03 96.23% 3.76

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100%

Chapter 9 cumulatives %

88.26 88.26%

6.54 94.8%

1.44 96.24%

3.76 100%

Chapter 10 cumulatives %

88.8 88.8%

6.7 95.5%

1.62 97,12%

2.88 100%

Chapter 11 cumulatives %

76.6 76.6%

10.84 87.44%

2.42 89.86%

8.14 100%

Chapter 12 cumulatives %

78.85 78.85%

7.31 86.16%

3.65 89.81%

10.19 100%

Chapter 13 cumulatives %

75.92 75.92%

10.94 86.86%

3.28 90.14%

9.85 100%

Chapter 14 cumulatives %

92.9 92.9%

4.77 97.67%

0.39 98.06

1.94 100%

Based on the Table 6, it shows that the differences of K-1, K-2, AWL, and Off-list words among the chapters were not very significant. In other words, the proportion of vocabulary items in each chapter was relatively similar. Contrast precentage was seen in the result of K-1 chapter 4, 5, 11, 12, and 13 which have lower (close to 80%) presentage rather than the other chapters. One more contrast percentage was in the AWL result on chapter 4, 5, 11, 12, and 13 which got result close to 90%. That was happened because total words in the textbook in chapter 4, 5, 11, 12, and 13 were lower rather than the other chapters which have higher total words.

However, AWL group show the smallest percentage rather than the other group. It means that teachers should pay attention to those words since the AWL words are academic words that need more attention rather than other groups. The cumulative percentages of the K-1 and K-2 words also provide information about difficulty of understanding the textbooks since the percentages were below 97%, a necessary requirement for good understanding of a text.

28 C.2. Text comparison of the textbook:

C.2.1. Text comparison Chapter 5 vs. Chapter 14

This section presents the comparison of text between two chapters. The comparison shows the token recycling index of the chapters being compared. Recycling index is the ratio between words that are shared by two chapters and the total number of words in the second chapter. This index provides useful information about the words that similar or shared in both chapters and words which are new or unique in the second chapter. For teaching purposes, teachers need to pay attention to those words that are unique in the second chapter.

The first comparison is between Chapter 5 and Chapter 14. The reason behind focusing compare the chapters were because the distance of K1 words between Chapter 5 and 14 were 23.53% which was farther rather than the other chapter. The analysis of comparison shows that the token recycling index was 68.50% indicating that as much as 524 of words in Chapter 5 and Chapter 14 were similar (shared). Thus, new or unique words in chapter 14 were as much as 765 words. The shared as well as unique words are displayed in the Table 7 below. The figure next to a word is the occurrences or frequency of that word in the book.

Table 7. Shared and Unique words in Chapter 5 vs. Chapter 14.

TOKEN _{Recycling Index: (524 repeated tokens : 765 tokens in new text) =} 68.50%

29 The complete table has been put in Appendix E. C.2.2. Text comparison Chapter 4 vs. Chapter 14

The second comparison was between chapter 4 and chapter 14. The reason behind focusing compare the chapters were because the distance of AWL words between chapter 4 and chapter 14 were 4.26% which was farther rather than the other chapter. The analysis of comparison shows that the token recycling index the amount was 65.75%, indicating that as much as 503 of words in chapter 4 and 14 were similar (shared). Thus, new or unique words in chapter 14 were 766 words. The shared as well as unique words are displayed in the Table 8 below. The figure next to a word is the occurrences or frequency of that word in the book.

Table 8.Comparison of Chapter 4 vs. Chapter 14

TOKEN _{Recycling Index: (503 repeated tokens : 765 tokens in new text) =} 65.75%

30 The complete table has been put in Appendix F. CONCLUSION

The findings have presented identification of vocabulary profile from Act Locally Think Globally’ book. The study disclosed three results of what is vocabulary profile of the ninth grade Englsh textbook, what is the proportion of vocabulary that is not covered in the textbook, and what is the token (words) recycling index of the textbook. The result of overall vocabulary profile in the textbook showed the proportions of the vocabulary frequency classifications. For K-1 words (1-1000) was as much as 81.22%, K2 words (1001-2000) was as much as 8.89%, AWL words was as much as 2.01%, and the amount of Off List words was 7.88%.

The second result revealed negative vocabulary profiles of K-1, K-2, and AWL with lists of the vocabulary items that were not found in the textbook, also block frequency output of off-list words. For K-1total word families as much as 964 words, only 268 words were not in input. Then, K-2 total word families as many as 968 words, and 620 words were not in input. Following by AWL total word

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families 569 words, which 132 words were not in input. The last but not least there were 1704 tokens words and 543 types of Off-list words.

The last result disclosed vocabulary frequency levels every chapter and two comparison of chapters which was contrast. For chapter 5 and chapter 14 which focus on K-1 contrast revealed that there was 68.50% token recycling index, 524 words were similar, and 765 new words were found in the chapter 14. Followed by comparison of chapter 4 and 14 which focus on AWL contrast revealed there was 65.75% token recycling index, 503 words were similar, and 766 new words were found in the chapter 14.

Besides, the generality of these findings are limited since this study only analyzed IX graders book only. It will be advantageous for further research to analyze books grade VII and VIII in order getting richer data of the study which is used in SMPN 7 Salatiga. The implication of the study is I hope the result of vocabulary profiler may helpful for teacher to use as guidance and a reference to check the suitable words from textbook that they will teach to the students in the class. However, it also will be helpful for teacher to give assignment, task, or homework for the students by recognizing the level of difficulty of the vocabulary.

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Selecting vocabulary: Academic word list. .d . Ca ridge Ad a e Lear er s Di tio ar . Retrieved October 7, 2015, from http://www.uefap.com/vocab/select/awl.htm.

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ACKNOWLEDGEMENT

This study cannot be completed without many supports and helps. First, I would like to thank to Allah SWT for giving me spirit and strength to finish this study. I would like to express my sincere gratitude to my supervisor, Anne Indrayanti Timotius, M.Ed. for her invaluable patience and guidance during the completion of this study, and also my examiner, Prof. Dr. Gusti Astika, M.A for his guidance for this thesis. I also would like to say thank you for teachers in SMPN 7 Salatiga who guiding and giving me advice related to this study.

I would like to express gratitude to my beloved mom (Choiriyah), dad (Suwandi), sister (Gias), nephew (Jorrel), and niece (Joccel) for their immeasurable love and support to finish this study. Many thanks for my friends: Dwi, Ika, Herlina, and Melly for their support to finish this study. I also would like to say thank you for Irfan Allan Saputra who always stays by my side. Indeed, I would like to express my appreciation gratitude for my angkatan (Twelvers), all lectures, and staffs in Faculty of Language and Literature, Satya Wacana Christian University for all knowledge and things that have been shared.

34

Append

i

x A

K1 word families that are not found in the input textbooks ACCOUNT ACCOUNTABL E ACROSS ACTOR ACTRESS ADMIT ADVANCE ADVANTAGE ADVENTURE AFFAIR AGAINST AGE ALLOW ALONG ANCIENT APPEAR APPOINT ARISE ARM ARMY ARRIVE ARTICLE ATTACK ATTEMPT AVERAGE BALL BAR BATTLE BEGIN BENEATH BESIDE BEYOND BILL BLOW BLUE BRIDGE BROAD BUSINESS CAPITAL CASE CASTLE CHARGE CHIEF CHURCH CIRCLE CLAIM COAL COAST COIN COLLEGE COLONY COMMAND COMMITTEE COMPANY CONDITION CONSIDER CONTINUE CONTROL CORN COST COUNCIL COUNT CROWD CROWN CURRENT DAUGHTER DEAL DEAR DECLARE DEMAND DEPARTMENT DESERT DESIRE DESTROY DETERMINE DEVELOP DISCOVER DISTINGUISH DISTRICT DOLLAR DOUBT DROP DUTY EARTH EFFICIENT EFFORT ELECT ELEVEN ELSE EMPIRE EMPLOY ENEMY EQUAL ESCAPE EXCHANGE EXIST EXPERIMENT FAIR FAITH FAMILIAR FAMOUS FEAR FELLOW FIGHT FIGURE FIT FLOW FORCE FORMER FORTH GAIN GAME GARDEN GAS GENTLE HARDLY HEAVEN HONOUR HORSE HUSBAND ILL INCH INCREASE INDEED INDUSTRY INFLUENCE IRON JOINT JOY JUDGE JUSTICE KING LACK LADY LATTER LAUGHTER LAW LETTER LIMIT LIP LITERATURE LORD LOSS MANUFACTURE MASS MASTER MEMORY MERE MIGHT MINER MINISTER MOREOVER MRS NATIVE NEITHER NEWS NINE NOBLE NUMERICAL NUMEROUS OFF OFFICIAL OPPORTUNITY ORDINARY ORGANIZE OTHERWISE OUGHT OWE PARTICULAR PEACE PERHAPS POLITICAL POPULATION POSITION POSSESS POST POVERTY PRESIDENT PRESSURE PRIVATE PROOF PROVE PROVISION PUBLIC QUALITY QUANTITY QUEEN RANK RATE RECEIPT RECEIVE RECENT RECOGNIZE RECORD REGARD REMAIN REMARK REPUBLIC RESERVE RING RISE ROLL ROUGH ROYAL SAIL SALE SCARCE SEAT SECRETARY SENSE SENSITIVE SHADOW SHALL SHIP SHOOT SHORE SILENCE SILVER SIR SMILE SNOW SOCIETY SOLDIER SORT SOUL SPEED SPEND SPIRIT SPITE SPOT SQUARE STANDARD STATION STEEL STOCK STONE

35 STRANGE STRENGTH STRIKE STRUGGLE SUBSTANCE SUFFER SUMMER SUPPLY SUPPOSE SURFACE SURROUND SWEET SWORD SYSTEM TAX TEAR TEMPLE TERM THIRTEEN THOUGH THROW THUS TON TOTAL TOWARD TRAVEL TROUBLE TRUST TUESDAY UNION UNIVERSITY UNLESS UPON VALLEY VICTORY VIRTUE VOTE WAGE WAVE WESTERN WHETHER WIDE WIND WINDOW WINTER WISE WITHOUT WORTH YIELD

Append

i

x B

K2 word families that are not found in the input textbooks ABROAD ABSENT ABSOLUTE ABSOLUTELY ACCUSE ACCUSTOM ADMIRE AEROPLANE AFFORD AGRICULTURE AIM AIRPLANE ALIKE ALIVE ALOUD ALTOGETHER AMBITION AMUSE ANGLE ANXIETY APART APOLOGIZE APOLOGY APPLAUD APPROVE ARCH ARRANGE ARREST ARROW ASH ASHAMED ASIDE ASTONISH AUTUMN AVENUE AWAKE AWKWARD AXE BAGGAGE BALANCE BAND BARBER BARE BARELY BARGAIN BARREL BASIN BATHE BAY BEAK BEAM BEARD BEAST BEAT BEHAVE BELL BELT BEND BERRY BILLION BIND BITE BLADE BLAME BLESS BLIND BLOCK BOAST BOLD BONE BORDER BORROW BOTTLE BOTTOM BOUND BOUNDARY BOW BRAIN BRASS BREATH BRIBE BRICK BROADCAST BROWN BRUSH BUCKET BUNCH BUNDLE BURIAL BURST BURY BUSH BUTTON CALM CAMERA CAMP CANAL CAPE CARD CARRIAGE CAT CENT CENTIMETRE CENTURY CHAIN CHALK CHARM CHEAP CHEESE CHEQUE CHEST CHIMNEY CHRISTMAS CIVILISE CLERK CLIFF COAT COLLAR COMB COMFORT COMMERCE COMPANION COMPARE COMPETE COMPLAIN COMPOSE COMPLICATE CONFESS CONFIDENCE CONNECT CONQUER CONSCIENCE CONSCIOUS CONVENIENCE COPPER CORK CORNER COTTAGE COURAGE COWARD CRACK CRASH CREEP CRIMINAL CRITIC CROP CRUEL CULTIVATE CURIOUS CURL CURVE CUSHION CUSTOM DAMP DANCE DEAF DEBT DECAY DECEIVE DECREASE DEFEND DELAY DELIGHT DESPAIR DEVIL DIG DIP DISCIPLINE DISGUST DISMISS

36 DIVE DONKEY DOT DOUBLE DOZEN DRAG DRAWER DROWN DRUM DUCK DULL EAGER EARNEST EDGE EDUCATE ELASTIC ELDER ELECTRIC ELEPHANT EMPTY ENCLOSE ENCOURAGE ENGINE ENTIRE ENVELOPE ENVY ESSENCE ESSENTIAL EVIL EXAMINING EXCELLENT EXCESS EXCITE EXPLODE EXPLORE EXTRAORDINA RY FADE FAINT FALSE FAN FANCY FASHION FASTEN FATE FAULT FEAST FIERCE FILM FLASH FLAT FLESH FLOOD FOLD FOND FOOL FORBID FORGIVE FORK FORWARD FREEZE FREQUENT FRIGHT FUR GALLON GAP GARAGE GAY GOAT GRACE GRADUAL GRAMMAR GRASS GRATEFUL GRAVE GREASE GREY GRIND GUESS GUEST GUILTY GUN HANDKERCHIEF HARBOR HARVEST HASTE HAT HATE HAY HEAL HEAP HESITATE HINDER HIRE HOLLOW HOLY HONEST HOOK HOST HOTEL HUMBLE HURRAH HURRY HURT HUT IDEAL IDLE IMAGINE IMITATE IMMENSE IMPROVE INFORMAL INFORMALLY INN INQUIRE INSULT INSURE INTERFERE INTERNATION AL INTERRUPT INVENT INWARD ISLAND JAW JEALOUS JEWEL JOKE JOURNEY JUICE KEY KILOGRAM KILOMETRE KISS KNEEL LADDER LAMP LEAN LEND LIBERTY LIMB LITRE LOAF LOAN LODGING LOG LONE LOOSE LOYAL MAD MAIL MAP MAT MEANTIME MEANWHILE MELT MEND MERCHANT MERCY MERRY MESSENGER METRE MILL MILLIGRAM MILLILITRE MILLIMETRE MINERAL MISERABLE MODEST MONKEY MOTION MULTIPLY MURDER MYSTERY NARROW NEAT NEEDLE NEGLECT NEST NONSENSE NOON NOSE NOUN NUISANCE NURSE NUT OAR OBEY OCEAN OFFEND OMIT ONWARDS OPPOSE ORANGE ORGAN ORNAMENT OUTLINE OVERCOME PAD PARCEL PARDON PASSAGE PASSENGER PASTE PATH PATIENT PATRIOTIC PATTERN PAUSE PAW PEARL PECULIAR PEN PENCIL PENNY PERMANENT PERSUADE PET PHOTOGRAPH PIG PIGEON PILE PIN PINCH PINK PIPE PITY PLANE PLASTER PLENTY PLURAL POEM POISON POLITE POOL POSTPONE POT POWDER PRACTICAL PREACH PRECIOUS PREJUDICE PRESERVE PRETEND PRIDE PRIEST PRINT PRISON PROBABLE PROCESSION PROFESSION

37 PROGRAMME PROMPT PRONOUNCE PUNCTUAL PUNISH PUPIL PURE PURPLE PUSH PUZZLE QUALIFY QUARREL QUART RABBIT RADIO RAIL RAKE RAPID RAT RAW RAY RAZOR REFRESH REGRET REJOICE REMEDY RENT REPAIR REPRODUCE REPUTATION REQUEST RESCUE RESIGN RESPONSIBLE RESTAURANT RETIRE REVENGE REVIEW REWARD RIBBON RISK RIVAL ROAR ROAST ROB ROD ROOF ROOT ROPE ROT ROW RUDE RUG RUIN RUSH RUST SACRED SACRIFICE SADDLE SAKE SALARY SAMPLE SATISFY SAUCER SAWS SCATTER SCENT SCISSORS SCOLD SCORN SCRATCH SCREEN SCREW SEIZE SELDOM SEVERE SHADE SHALLOW SHAME SHAVE SHEEP SHEET SHELL SHIELD SHILLING SHOCK SHOUT SHUT SIGNAL SINCERE SLAVE SLIDE SMOOTH SNAKE SOAP SOCK SOIL SOLEMN SOLVE SOUP SOUR SOW SPADE SPARE SPILL SPIN SPIT SPLENDID SPLIT SPOIL STAIN STAMP STEADY STEAL STEER STEM STIFF STING STOCKING STORM STOVE STRAW STRETCH STRICT STRING STRIP STRIPE STUPID SUDDEN SUPPER SUSPECT SUSPICION SWALLOW SWEAR SWEAT SWELL SWING SYMPATHY TAIL TALL TAP TAXI TELEGRAPH TEMPT TEND THEATRE THIRST THORN THREAT THUNDER TICKET TIDE TIGHT TIN TOBACCO TOE TONGUE TOWER TRACK TRAP TRAY TREASURE TREMBLE TRIBE TRICK TUBE TUNE TYPICAL UGLY UNIVERSE UPPER UPRIGHT UPSET UPWARDS URGE VAIN VEIL VERSE VIOLENT VOWEL VOYAGE WAIST WANDER WAX WEATHER WEED WHEEL WHIP WHISPER WICKED WIDOW WINE WIPE WIRE WITNESS WORM WORSHIP WRECK WRIST YELLOW ZERO

38

Append

i

x

C

AWL word families that are not found in the input textbooks ABANDON ABSTRACT ACADEMY ACCESS ACCOMMODATE ACCUMULATE ACCURATE ACKNOWLEDGE ACQUIRE ADEQUATE ADJACENT ADMINISTRATE ADVOCATE AFFECT AGGREGATE AID ALBEIT ALLOCATE ALTER ALTERNATIVE AMBIGUOUS AMEND ANALOGY ANNUAL ANTICIPATE APPARENT APPEND APPRECIATE APPROXIMATE ARBITRARY ASPECT ASSEMBLE ASSESS ASSIGN ASSIST ASSUME ASSURE ATTACH ATTAIN ATTRIBUTE AUTHOR AUTHORITY AWARE BEHALF BIAS BOND BRIEF BULK CAPABLE CATEGORY CEASE CHALLENGE CHANNEL CHART CIRCUMSTANCE CITE CIVIL CLASSIC CLAUSE CODE COHERENT COINCIDE COLLAPSE COLLEAGUE COMMENCE COMMENT COMMISSION COMMIT COMMODITY COMPATIBLE COMPENSATE COMPILE COMPLEMENT COMPONENT COMPOUND COMPREHENSIVE COMPRISE CONCEIVE CONCEPT CONCLUDE CONCURRENT CONDUCT CONFER CONFINE CONFIRM CONFLICT CONFORM CONSENT CONSEQUENT CONSIDERABLE CONSTANT CONSTITUTE CONSTRAIN CONSTRUCT CONSULT CONSUME CONTEMPORARY CONTRADICT CONTRARY CONTRIBUTE CONTROVERSY CONVENE CONVERSE CONVERT CONVINCE COOPERATE COORDINATE CORPORATE COUPLE CREDIT CRITERIA CRUCIAL CURRENCY CYCLE DATA DEBATE DECLINE DEDUCE DEFINITE DEMONSTRATE DENOTE DENY DEPRESS DERIVE DESPITE DETECT DEVIATE DEVICE DEVOTE DIMENSION DIMINISH DISCRETE DISCRIMINATE DISPLACE DISPLAY DISPOSE DISTORT DISTRIBUTE DIVERSE DOCUMENT DOMAIN DOMINATE DRAFT DRAMA DURATION DYNAMIC ECONOMY ELIMINATE EMERGE EMPHASIS EMPIRICAL ENABLE ENFORCE ENORMOUS ENTITY EQUATE EQUIP ERODE ERROR ESTABLISH ESTATE ESTIMATE ETHIC ETHNIC EVALUATE EVENTUAL EVOLVE EXCEED EXCLUDE EXHIBIT EXPAND EXPERT EXPLOIT EXPORT FACILITATE FACTOR FEATURE FEDERAL FEE FILE FINANCE FINITE FLEXIBLE FLUCTUATE FORMAT FORTHCOMING FOUNDATION FRAMEWORK FUND FUNDAMENTAL FURTHERMORE GENDER GENERATE GENERATION GLOBE GUARANTEE GUIDELINE HENCE HIERARCHY HIGHLIGHT HYPOTHESIS IDENTICAL IDEOLOGY IGNORANT ILLUSTRATE IMAGE IMMIGRATE IMPACT IMPLEMENT IMPLICATE IMPLICIT IMPLY IMPOSE INCENTIVE INCIDENCE INCLINE INCOME INCORPORATE INDEX INDUCE INEVITABLE INFER INFRASTRUCT URE INHERENT INHIBIT INITIAL INITIATE INNOVATE INPUT INSERT INSIGHT INSPECT INSTANCE INSTITUTE

39 INTEGRAL INTEGRATE INTEGRITY INTELLIGENCE INTENSE INTERMEDIATE INTERNAL INTERPRET INTERVAL INTERVENE INTRINSIC INVEST INVESTIGATE INVOKE INVOLVE ISOLATE ISSUE JUSTIFY LABOUR LAYER LECTURE LEGAL LEGISLATE LEVY LIBERAL LIKEWISE LINK LOCATE LOGIC MAINTAIN MAJOR MANIPULATE MARGIN MECHANISM MEDIA MEDIATE MENTAL MIGRATE MILITARY MINIMAL MINIMISE MINIMUM MINISTRY MODE MODIFY MONITOR MOTIVE MUTUAL NEGATE NETWORK NEUTRAL NEVERTHELES S NONETHELESS NORM NOTION NOTWITHSTAN DING OBJECTIVE OBVIOUS OCCUPY OCCUR ODD OFFSET ONGOING ORIENT OUTCOME OUTPUT OVERALL OVERLAP OVERSEAS PARADIGM PARALLEL PARAMETER PARTNER PERCEIVE PERCENT PERIOD PERSIST PERSPECTIVE PHASE PHILOSOPHY PLUS POLICY PORTION POSE POSITIVE POTENTIAL PRACTITIONER PRECEDE PRECISE PREDICT PREDOMINANT PRELIMINARY PRESUME PRIMARY PRIME PRINCIPLE PRIOR PRIORITY PROHIBIT PROPORTION PROSPECT PROTOCOL PSYCHOLOGY PUBLICATION PURCHASE PURSUE QUALITATIVE QUOTE RADICAL RANDOM RANGE RATIO RATIONAL REACT REFINE REGIME REGION REGULATE REINFORCE REJECT RELAX RELEVANT RELUCTANCE RELY RESEARCH RESIDE RESTORE RESTRAIN RESTRICT RETAIN REVEAL REVENUE REVISE REVOLUTION RIGID ROUTE SCENARIO SCHEDULE SCHEME SCOPE SECTOR SELECT SEQUENCE SERIES SEX SHIFT SIGNIFICANT SIMULATE SITE SO-CALLED SOMEWHAT SOURCE SPECIFIC SPECIFY SPHERE STABLE STATISTIC STATUS STRAIGHTFOR WARD STRATEGY STRESS STYLE SUBMIT SUBORDINATE SUBSEQUENT SUBSIDY SUBSTITUTE SUCCESSOR SUFFICIENT SUM SUPPLEMENT SURVEY SUSPEND SUSTAIN TAPE TARGET TECHNICAL TECHNIQUE TECHNOLOGY THEME THEORY THEREBY THESIS TOPIC TRACE TRANSFER TRANSFORM TRANSIT TRANSMIT TRANSPORT TREND TRIGGER ULTIMATE UNDERGO UNDERLIE UNDERTAKE UNIFY UTILISE VALID VIA VIOLATE VIRTUAL VISIBLE VISION VISUAL VOLUME VOLUNTARY WELFARE WHEREAS WHEREBY WIDESPREAD

40

Appendix D

Block frequency output of off-list words.

RANK FREQ COVERAGE

individ cumulative WORD

1. 66 3.87% 3.87% PUNCTUATION 2. 37 2.17% 6.04% ORALLY

3. 36 2.11% 8.15% BUFFALO 4. 28 1.64% 9.79% ORPHAN 5. 26 1.53% 11.32% INGREDIENTS 6. 23 1.35% 12.67% DRUG

7. 21 1.23% 13.90% ORPHANAGE 8. 20 1.17% 15.07% DIALOGUE 9. 19 1.12% 16.19% MAGAZINE 10. 19 1.12% 17.31% RECIPE 11. 18 1.06% 18.37% BATS

12. 16 0.94% 19.31% HOMEWORK 13. 16 0.94% 20.25% SERA

14. 15 0.88% 21.13% CLASSROOM 15. 15 0.88% 22.01% COCONUT 16. 15 0.88% 22.89% NOVEL 17. 15 0.88% 23.77% UNDERLINE 18. 13 0.76% 24.53% ENCYCLOPEDIA 19. 13 0.76% 25.29% FABRICS

20. 13 0.76% 26.05% HANDWRITE 21. 12 0.70% 26.75% EXPIRATION 22. 12 0.70% 27.45% ORAL

41 24. 11 0.65% 28.75% BANANA 25. 11 0.65% 29.40% COLUMN 26. 11 0.65% 30.05% CONDENSED 27. 11 0.65% 30.70% MAMMALS 28. 11 0.65% 31.35% RAVEN 29. 10 0.59% 31.94% DIARRHEA 30. 10 0.59% 32.53% DOSAGE 31. 10 0.59% 33.12% KIDS 32. 10 0.59% 33.71% SWITCH 33. 9 0.53% 34.24% CELEBRATE 34. 9 0.53% 34.77% COCKTAIL 35. 9 0.53% 35.30% FOLKTALE 36. 9 0.53% 35.83% PM

37. 8 0.47% 36.30% AFRICAN 38. 8 0.47% 36.77% CANTEEN 39. 8 0.47% 37.24% CLUES 40. 8 0.47% 37.71% FOXES 41. 8 0.47% 38.18% MATH 42. 8 0.47% 38.65% PUDDING 43. 8 0.47% 39.12% SYRUP

44. 7 0.41% 39.53% INGREDIENT 45. 7 0.41% 39.94% MOTORCYCLE 46. 7 0.41% 40.35% OLIVE

47. 7 0.41% 40.76% PLASTIC 48. 7 0.41% 41.17% RECIPES 49. 7 0.41% 41.58% TRAFFIC 50. 7 0.41% 41.99% VEGETABLES 51. 6 0.35% 42.34% BAT

52. 6 0.35% 42.69% DOUGH

53. 6 0.35% 43.04% HANDICRAFTS 54. 6 0.35% 43.39% HORNS

42 56. 6 0.35% 44.09% MESSY

57. 6 0.35% 44.44% PEDESTRIANS 58. 6 0.35% 44.79% SLICED

59. 6 0.35% 45.14% TRASH 60. 5 0.29% 45.43% ATTICS 61. 5 0.29% 45.72% AWARD 62. 5 0.29% 46.01% BADMINTON 63. 5 0.29% 46.30% BATHROOM 64. 5 0.29% 46.59% BEEF

65. 5 0.29% 46.88% BLANK 66. 5 0.29% 47.17% FABRIC 67. 5 0.29% 47.46% FIBERS 68. 5 0.29% 47.75% GOODS 69. 5 0.29% 48.04% GREEK 70. 5 0.29% 48.33% HOBBY

71. 5 0.29% 48.62% HOUSEWORK 72. 5 0.29% 48.91% JAVA

73. 5 0.29% 49.20% NOVELS 74. 5 0.29% 49.49% OREGANO 75. 5 0.29% 49.78% SPECIES 76. 5 0.29% 50.07% SUNSET 77. 4 0.23% 50.30% ABSORBENT 78. 4 0.23% 50.53% AMAZING 79. 4 0.23% 50.76% AUSTRALIA 80. 4 0.23% 50.99% CHARITY 81. 4 0.23% 51.22% CHOIR

82. 4 0.23% 51.45% CONJUNCTION 83. 4 0.23% 51.68% CRYSTALS 84. 4 0.23% 51.91% DAMSELFLY 85. 4 0.23% 52.14% DECORATION 86. 4 0.23% 52.37% DIGEST

43 88. 4 0.23% 52.83% ETC

89. 4 0.23% 53.06% FIBER

90. 4 0.23% 53.29% FINGERTIPS 91. 4 0.23% 53.52% HANDICRAFT 92. 4 0.23% 53.75% HAPPENINGS 93. 4 0.23% 53.98% HEATPROOF 94. 4 0.23% 54.21% HERDS 95. 4 0.23% 54.44% INDONESIA 96. 4 0.23% 54.67% JAMS

97. 4 0.23% 54.90% LAMB

98. 4 0.23% 55.13% LENGTHWISE 99. 4 0.23% 55.36% LUMPY

100. 4 0.23% 55.59% MARGARINE 101. 4 0.23% 55.82% MARINATED 102. 4 0.23% 56.05% MILILITRES 103. 4 0.23% 56.28% NEEDY 104. 4 0.23% 56.51% PACKAGING 105. 4 0.23% 56.74% PEELED 106. 4 0.23% 56.97% PROVINCE 107. 4 0.23% 57.20% RUPIAHS 108. 4 0.23% 57.43% SALAD 109. 4 0.23% 57.66% SCOOP 110. 4 0.23% 57.89% SCOUT 111. 4 0.23% 58.12% SHREDDED 112. 4 0.23% 58.35% SIFTED 113. 4 0.23% 58.58% SPICY 114. 4 0.23% 58.81% SPONGE 115. 4 0.23% 59.04% SPONGES 116. 4 0.23% 59.27% STARCH 117. 4 0.23% 59.50% SWAMPS 118. 4 0.23% 59.73% TAPIOCA 119. 4 0.23% 59.96% TERRITORY

44 120. 4 0.23% 60.19% THERMAL 121. 4 0.23% 60.42% TOFU 122. 4 0.23% 60.65% TV

123. 4 0.23% 60.88% VEGETABLE 124. 4 0.23% 61.11% WALLOW 125. 4 0.23% 61.34% WATERPROOF 126. 4 0.23% 61.57% WETTER 127. 3 0.18% 61.75% ACROBATIC 128. 3 0.18% 61.93% AFRICA 129. 3 0.18% 62.11% ALCOHOL 130. 3 0.18% 62.29% ASPIRIN 131. 3 0.18% 62.47% AWESOME 132. 3 0.18% 62.65% BAMBOO 133. 3 0.18% 62.83% BANANAS 134. 3 0.18% 63.01% BIKE 135. 3 0.18% 63.19% BOUNCE 136. 3 0.18% 63.37% BRACKETS 137. 3 0.18% 63.55% CERAMICS 138. 3 0.18% 63.73% CHASE 139. 3 0.18% 63.91% CHORES 140. 3 0.18% 64.09% CM

141. 3 0.18% 64.27% COVERINGS 142. 3 0.18% 64.45% DICTATE 143. 3 0.18% 64.63% DONNY 144. 3 0.18% 64.81% DOWNLOAD 145. 3 0.18% 64.99% EARPHONES 146. 3 0.18% 65.17% ECHOES 147. 3 0.18% 65.35% FURRY 148. 3 0.18% 65.53% GARLIC

149. 3 0.18% 65.71% GRASSHOPPERS 150. 3 0.18% 65.89% GRILL

45 152. 3 0.18% 66.25% GUITAR 153. 3 0.18% 66.43% HABITAT 154. 3 0.18% 66.61% HENS 155. 3 0.18% 66.79% INFO 156. 3 0.18% 66.97% JUNIOR 157. 3 0.18% 67.15% MAYOR

158. 3 0.18% 67.33% MEANINGFULLY 159. 3 0.18% 67.51% METER

160. 3 0.18% 67.69% ML 161. 3 0.18% 67.87% MOP

162. 3 0.18% 68.05% MOSQUITOES 163. 3 0.18% 68.23% MUSCLES 164. 3 0.18% 68.41% NOCTURNAL 165. 3 0.18% 68.59% NUTRITION 166. 3 0.18% 68.77% ORPHANS 167. 3 0.18% 68.95% OVERDOSE 168. 3 0.18% 69.13% OXYGEN 169. 3 0.18% 69.31% PALM 170. 3 0.18% 69.49% PITCHED 171. 3 0.18% 69.67% PLUG 172. 3 0.18% 69.85% POLLEN 173. 3 0.18% 70.03% POLLUTION 174. 3 0.18% 70.21% PREY

175. 3 0.18% 70.39% REDUCER 176. 3 0.18% 70.57% RELIEVER 177. 3 0.18% 70.75% RESTATE 178. 3 0.18% 70.93% RINSE 179. 3 0.18% 71.11% RINSED 180. 3 0.18% 71.29% SANDALS 181. 3 0.18% 71.47% SCHOLARSHIP 182. 3 0.18% 71.65% SKEWERED 183. 3 0.18% 71.83% SLIPPERY

46 184. 3 0.18% 72.01% SQUEALS 185. 3 0.18% 72.19% SYRUPY

186. 3 0.18% 72.37% TEASPOONFUL 187. 3 0.18% 72.55% TROPICAL 188. 3 0.18% 72.73% UNPLUG 189. 3 0.18% 72.91% YOUTUBE

190. 2 0.12% 73.03% ACETAMINOPHEN 191. 2 0.12% 73.15% AVOCADO

192. 2 0.12% 73.27% BACTERIA 193. 2 0.12% 73.39% BARLEY 194. 2 0.12% 73.51% BATIK 195. 2 0.12% 73.63% BEACH 196. 2 0.12% 73.75% BENNY 197. 2 0.12% 73.87% BIN 198. 2 0.12% 73.99% BINS 199. 2 0.12% 74.11% BOSS

200. 2 0.12% 74.23% BOYFRIEND 201. 2 0.12% 74.35% BUBBLING 202. 2 0.12% 74.47% CAMPUS 203. 2 0.12% 74.59% CARVING 204. 2 0.12% 74.71% CASUAL 205. 2 0.12% 74.83% CELSIUS 206. 2 0.12% 74.95% CHILLY 207. 2 0.12% 75.07% CHOCOLATE 208. 2 0.12% 75.19% CLASSMATES 209. 2 0.12% 75.31% COACH

210. 2 0.12% 75.43% COCKERELS 211. 2 0.12% 75.55% CONDUCTORS 212. 2 0.12% 75.67% COOKIES 213. 2 0.12% 75.79% CORD 214. 2 0.12% 75.91% CRAFT 215. 2 0.12% 76.03% CRITICIZE

47 216. 2 0.12% 76.15% CROW 217. 2 0.12% 76.27% CUBED 218. 2 0.12% 76.39% CUBES 219. 2 0.12% 76.51% DISSOLVES 220. 2 0.12% 76.63% DONATED 221. 2 0.12% 76.75% DRAGONFLIES 222. 2 0.12% 76.87% DRUGS

223. 2 0.12% 76.99% DUSTY 224. 2 0.12% 77.11% EBONY 225. 2 0.12% 77.23% ELDEST 226. 2 0.12% 77.35% EMAIL

227. 2 0.12% 77.47% ENCYCLOPEDIAS 228. 2 0.12% 77.59% ENTITLED

229. 2 0.12% 77.71% FOREHEAD 230. 2 0.12% 77.83% GARBAGE 231. 2 0.12% 77.95% GEMS 232. 2 0.12% 78.07% GENIES 233. 2 0.12% 78.19% GILLS

234. 2 0.12% 78.31% GRADUATION 235. 2 0.12% 78.43% GRASSHOPPER 236. 2 0.12% 78.55% GRATED

237. 2 0.12% 78.67% GUAVA 238. 2 0.12% 78.79% HABITATS 239. 2 0.12% 78.91% IMPERATIVE 240. 2 0.12% 79.03% INSULATORS 241. 2 0.12% 79.15% IRREFUTABLE 242. 2 0.12% 79.27% JACKFRUIT 243. 2 0.12% 79.39% JOG

244. 2 0.12% 79.51% LEAP 245. 2 0.12% 79.63% LINEN 246. 2 0.12% 79.75% LYRIC 247. 2 0.12% 79.87% MARINATE

48 248. 2 0.12% 79.99% MOTORCYCLES 249. 2 0.12% 80.11% MULLET

250. 2 0.12% 80.23% MUSCLE 251. 2 0.12% 80.35% NON 252. 2 0.12% 80.47% NOODLE 253. 2 0.12% 80.59% NYMPHS

254. 2 0.12% 80.71% OHH&#NUMBER;OHH&#NUMBER;OHH 255. 2 0.12% 80.83% ORES

256. 2 0.12% 80.95% PANTS 257. 2 0.12% 81.07% PASTRY 258. 2 0.12% 81.19% PAVEMENTS 259. 2 0.12% 81.31% PEEL

260. 2 0.12% 81.43% PHYSICIAN 261. 2 0.12% 81.55% PILLOWS 262. 2 0.12% 81.67% PLASTICS 263. 2 0.12% 81.79% POLLUTED 264. 2 0.12% 81.91% PRACTICED 265. 2 0.12% 82.03% PREDATOR 266. 2 0.12% 82.15% PRESERVATIVES 267. 2 0.12% 82.27% PUPPETEER 268. 2 0.12% 82.39% PUPPETS 269. 2 0.12% 82.51% REWRITE 270. 2 0.12% 82.63% ROUTINELY 271. 2 0.12% 82.75% SANDSTONE 272. 2 0.12% 82.87% SAUCEPAN 273. 2 0.12% 82.99% SCAVENGER 274. 2 0.12% 83.11% SENSATION 275. 2 0.12% 83.23% SENSATIONAL 276. 2 0.12% 83.35% SHRED

277. 2 0.12% 83.47% SIFT 278. 2 0.12% 83.59% SKEWER 279. 2 0.12% 83.71% SOAK

49 280. 2 0.12% 83.83% SOAKS 281. 2 0.12% 83.95% SOY

282. 2 0.12% 84.07% SQUASHED 283. 2 0.12% 84.19% SUNDRIED 284. 2 0.12% 84.31% SWEETHEART 285. 2 0.12% 84.43% TAMARIND 286. 2 0.12% 84.55% TEAK 287. 2 0.12% 84.67% TEASPOON 288. 2 0.12% 84.79% TELESCOPE 289. 2 0.12% 84.91% TINY

290. 2 0.12% 85.03% TISSUES 291. 2 0.12% 85.15% TUB 292. 2 0.12% 85.27% VENDOR 293. 2 0.12% 85.39% WEDDING 294. 2 0.12% 85.51% WEDDINGS 295. 2 0.12% 85.63% WOVEN 296. 2 0.12% 85.75% WOW 297. 1 0.06% 85.81% ABSORB 298. 1 0.06% 85.87% ABSORBS 299. 1 0.06% 85.93% ACEH 300. 1 0.06% 85.99% ACHIEVER 301. 1 0.06% 86.05% ACID

302. 1 0.06% 86.11% ADDITIVES 303. 1 0.06% 86.17% AHMAD 304. 1 0.06% 86.23% AIRLESS 305. 1 0.06% 86.29% AMBON 306. 1 0.06% 86.35% AMERICA 307. 1 0.06% 86.41% ANIDAN 308. 1 0.06% 86.47% ANT

309. 1 0.06% 86.53% ANTIOXIDANT 310. 1 0.06% 86.59% ANTIQUE 311. 1 0.06% 86.65% APPLIANCE

50 312. 1 0.06% 86.71% ARABIC 313. 1 0.06% 86.77% ASCORBIC 314. 1 0.06% 86.83% AVAIBALE

315. 1 0.06% 86.89% BACK&#NUMBER;BACK 316. 1 0.06% 86.95% BAKERY

317. 1 0.06% 87.01% BALSA 318. 1 0.06% 87.07% BARBEQUE 319. 1 0.06% 87.13% BENCH

320. 1 0.06% 87.19% BLACKBOARD 321. 1 0.06% 87.25% BLENDED 322. 1 0.06% 87.31% BRAND 323. 1 0.06% 87.37% BRANDS 324. 1 0.06% 87.43% BROADWAY 325. 1 0.06% 87.49% BULL

326. 1 0.06% 87.55% BULLS 327. 1 0.06% 87.61% BUMP 328. 1 0.06% 87.67% CAFFEINE 329. 1 0.06% 87.73% CALENDAR 330. 1 0.06% 87.79% CARBOHYDRATE 331. 1 0.06% 87.85% CARVERS

332. 1 0.06% 87.91% CARVINGS 333. 1 0.06% 87.97% CASSAVA 334. 1 0.06% 88.03% CASTER

335. 1 0.06% 88.09% CELEBRATION 336. 1 0.06% 88.15% CELEBRATIONS 337. 1 0.06% 88.21% CELL

338. 1 0.06% 88.27% CHAMPION 339. 1 0.06% 88.33% CHARCOAL 340. 1 0.06% 88.39% CHAT 341. 1 0.06% 88.45% CHATTING 342. 1 0.06% 88.51% CHILIES 343. 1 0.06% 88.57% CHOPPED

51 344. 1 0.06% 88.63% CIGAR

345. 1 0.06% 88.69% CIGARETTES 346. 1 0.06% 88.75% CLOVES 347. 1 0.06% 88.81% COCKROACH 348. 1 0.06% 88.87% COCKS

349. 1 0.06% 88.93% COCONUTS

350. 1 0.06% 88.99% COMMEMORATION 351. 1 0.06% 89.05% COMPULSORY 352. 1 0.06% 89.11% CONDITIONER 353. 1 0.06% 89.17% CONGRATULATION 354. 1 0.06% 89.23% CORIANDER

355. 1 0.06% 89.29% CRESTS 356. 1 0.06% 89.35% CUISINE 357. 1 0.06% 89.41% CURRICULAR 358. 1 0.06% 89.47% DAIRY

359. 1 0.06% 89.53% DAMSELFLIES 360. 1 0.06% 89.59% DAWN

361. 1 0.06% 89.65% DAYBREAK 362. 1 0.06% 89.71% DAYDREAM 363. 1 0.06% 89.77% DEHYDRATED 364. 1 0.06% 89.83% DESEEDED 365. 1 0.06% 89.89% DESSERT 366. 1 0.06% 89.95% DISCOUNT 367. 1 0.06% 90.01% DOLL 368. 1 0.06% 90.07% DONORS 369. 1 0.06% 90.13% DOSAGES 370. 1 0.06% 90.19% DOSE 371. 1 0.06% 90.25% DOSES

372. 1 0.06% 90.31% DRAGONFLY 373. 1 0.06% 90.37% DRAIN

374. 1 0.06% 90.43% DRAPED 375. 1 0.06% 90.49% DRIZZLE

52 376. 1 0.06% 90.55% EFFICIENTLY 377. 1 0.06% 90.61% ELEVATOR 378. 1 0.06% 90.67% EMBROIDERED 379. 1 0.06% 90.73% EMULSIFIER 380. 1 0.06% 90.79% ENT

381. 1 0.06% 90.85% ETERNAL 382. 1 0.06% 90.91% EVAPORATED 383. 1 0.06% 90.97% EXAM

384. 1 0.06% 91.03% EXEMPLARY

385. 1 0.06% 91.09% EXTRACURRICULAR 386. 1 0.06% 91.15% FARMYARD

387. 1 0.06% 91.21% FAUNA 388. 1 0.06% 91.27% FERMENTED 389. 1 0.06% 91.33% FINS

390. 1 0.06% 91.39% FLAMMABLE 391. 1 0.06% 91.45% FLIED

392. 1 0.06% 91.51% FLORA 393. 1 0.06% 91.57% FLU

394. 1 0.06% 91.63% FLUTTERING 395. 1 0.06% 91.69% FOSSILS 396. 1 0.06% 91.75% FOURTY 397. 1 0.06% 91.81% FRESHWATER 398. 1 0.06% 91.87% FULFILLED 399. 1 0.06% 91.93% GAEA 400. 1 0.06% 91.99% GEAE

401. 1 0.06% 92.05% GEOGRAPHY 402. 1 0.06% 92.11% GERMS 403. 1 0.06% 92.17% GLOOMY 404. 1 0.06% 92.23% GLUTINOUS 405. 1 0.06% 92.29% GRADUATIONS 406. 1 0.06% 92.35% GRANDEST 407. 1 0.06% 92.41% GUTTERS

53 408. 1 0.06% 92.47% GUYS

409. 1 0.06% 92.53% HANDSOME 410. 1 0.06% 92.59% HATCH 411. 1 0.06% 92.65% HEADACHE 412. 1 0.06% 92.71% HEADMASTER 413. 1 0.06% 92.77% HEARTED 414. 1 0.06% 92.83% HOP 415. 1 0.06% 92.89% HORN 416. 1 0.06% 92.95% IDR

417. 1 0.06% 93.01% IMPORTED 418. 1 0.06% 93.07% INACTIVE 419. 1 0.06% 93.13% INSISTED

420. 1 0.06% 93.19% INTERPERSONAL 421. 1 0.06% 93.25% JAKARTA

422. 1 0.06% 93.31% JAPANESE 423. 1 0.06% 93.37% JOGGING 424. 1 0.06% 93.43% KAPOK 425. 1 0.06% 93.49% KID

426. 1 0.06% 93.55% KILOMETERS 427. 1 0.06% 93.61% KNITTING 428. 1 0.06% 93.67% KNOB 429. 1 0.06% 93.73% KOREAN 430. 1 0.06% 93.79% KRATON 431. 1 0.06% 93.85% LABORATORY 432. 1 0.06% 93.91% LAUNCHED 433. 1 0.06% 93.97% LAYOUT 434. 1 0.06% 94.03% LIME 435. 1 0.06% 94.09% LITER 436. 1 0.06% 94.15% LITTER 437. 1 0.06% 94.21% LUXURY 438. 1 0.06% 94.27% MAGAZINES 439. 1 0.06% 94.33% MANADO

54 440. 1 0.06% 94.39% MANGOES 441. 1 0.06% 94.45% MATES 442. 1 0.06% 94.51% MILLILITERS 443. 1 0.06% 94.57% MODAL

444. 1 0.06% 94.63% MOISTURIZED 445. 1 0.06% 94.69% MOPPING 446. 1 0.06% 94.75% MOUNT 447. 1 0.06% 94.81% MSG 448. 1 0.06% 94.87% NETT

449. 1 0.06% 94.93% NONLIVING 450. 1 0.06% 94.99% NOTEBOOK 451. 1 0.06% 95.05% OAK

452. 1 0.06% 95.11% OATMEAL 453. 1 0.06% 95.17% ODER 454. 1 0.06% 95.23% OKAY 455. 1 0.06% 95.29% OLIVES 456. 1 0.06% 95.35% OPTIMISTIC 457. 1 0.06% 95.41% ORGANIC 458. 1 0.06% 95.47% OVERCOOK 459. 1 0.06% 95.53% PANTHER 460. 1 0.06% 95.59% PEANUT 461. 1 0.06% 95.65% PECK 462. 1 0.06% 95.71% PEELING 463. 1 0.06% 95.77% PICNIC 464. 1 0.06% 95.83% PIE

465. 1 0.06% 95.89% PLAYGROUNDS 466. 1 0.06% 95.95% PLEA

467. 1 0.06% 96.01% PLUGGING 468. 1 0.06% 96.07% POLYGLOT 469. 1 0.06% 96.13% PONDS 470. 1 0.06% 96.19% PRINCESS 471. 1 0.06% 96.25% PROTEIN

55 472. 1 0.06% 96.31% PUNCTUATED 473. 1 0.06% 96.37% RAINBOWS 474. 1 0.06% 96.43% REARED 475. 1 0.06% 96.49% REWRITTEN 476. 1 0.06% 96.55% RUFF

477. 1 0.06% 96.61% SACHET 478. 1 0.06% 96.67% SACHETS 479. 1 0.06% 96.73% SALTWATER 480. 1 0.06% 96.79% SATURATES 481. 1 0.06% 96.85% SCAR

482. 1 0.06% 96.91% SCULPTURE 483. 1 0.06% 96.97% SEAL

484. 1 0.06% 97.03% SENIOR 485. 1 0.06% 97.09% SHIMMER 486. 1 0.06% 97.15% SHOALS

487. 1 0.06% 97.21% SHUTTLECOCKS 488. 1 0.06% 97.27% SINA

489. 1 0.06% 97.33% SKEWERS 490. 1 0.06% 97.39% SKILLFUL 491. 1 0.06% 97.45% SKIP 492. 1 0.06% 97.51% SLICE 493. 1 0.06% 97.57% SMART 494. 1 0.06% 97.63% SODIUM 495. 1 0.06% 97.69% SOLO

496. 1 0.06% 97.75% SORETHROAT 497. 1 0.06% 97.81% SOUNDLY 498. 1 0.06% 97.87% SOUVENIR 499. 1 0.06% 97.93% SOUVENIRS 500. 1 0.06% 97.99% SOYA

501. 1 0.06% 98.05% SPINACH 502. 1 0.06% 98.11% SPRINKLED 503. 1 0.06% 98.17% SPRITZ

56 504. 1 0.06% 98.23% SPROUTS 505. 1 0.06% 98.29% SQUEEZED 506. 1 0.06% 98.35% STARVE 507. 1 0.06% 98.41% STEAK 508. 1 0.06% 98.47% STEAMER 509. 1 0.06% 98.53% SUDANESE 510. 1 0.06% 98.59% SUPER 511. 1 0.06% 98.65% SWAYED 512. 1 0.06% 98.71% SWEETEN 513. 1 0.06% 98.77% SWOOP

514. 1 0.06% 98.83% TABLESPOON 515. 1 0.06% 98.89% TABLESPOONS 516. 1 0.06% 98.95% TABLET

517. 1 0.06% 99.01% TABLETS 518. 1 0.06% 99.07% TAMPER 519. 1 0.06% 99.13% TEASPOONS 520. 1 0.06% 99.19% TEENAGER 521. 1 0.06% 99.25% TELLER 522. 1 0.06% 99.31% TEXTBOOKS 523. 1 0.06% 99.37% TOILET 524. 1 0.06% 99.43% TOILETS 525. 1 0.06% 99.49% TOLL

526. 1 0.06% 99.55% TOOTHACHE 527. 1 0.06% 99.61% TOSS

528. 1 0.06% 99.67% TRANSLATORS 529. 1 0.06% 99.73% TRANSPARENT 530. 1 0.06% 99.79% TRUCKS

531. 1 0.06% 99.85% TUTORING 532. 1 0.06% 99.91% UDDER

533. 1 0.06% 99.97% UNRECYCLABLE 534. 1 0.06% 100.00% VACCINATION 535. 1 0.06% 100.00% VENDORS

57 536. 1 0.06% 100.00% VICE

537. 1 0.06% 100.00% VIETNAM 538. 1 0.06% 100.00% VINEGAR 539. 1 0.06% 100.00% VIRGIN 540. 1 0.06% 100.00% VITAMIN 541. 1 0.06% 100.00% YAWNING 542. 1 0.06% 100.00% YEAST

543. 1 0.06% 100.00% YOGYAKARTA

Append

i

x E

Unique to first 1164 tokens 277 families

001. oral 28 002. rice 28 003. cup 26 004. recipe 25 005. cook 24 006. ingredient 23

007. act 22 008. apple 22 009. water 20 010. place 19 011. sugar 19 012. pan 17 013. analyse 14 014. coconut 14 015. flour 14 016. ice 14 017. steam 14 018. manual 13 019. each 12 020. leave 12 021. condense 11

022. milk 11 023. add 10 024. indicate 10

025. now 10 026. other 10 027. serve 10 028. switch 10 029. version 10 030. cocktail 9

Shared 524 tokens 105 families

001. you 68 002. the 36 003. i 26 004. a 23 005. it 19 006. to 17 007. can 14 008. in 14 009. this 13 010. and 12 011. come 12 012. of 10 013. if 9 014. have 8 015. know 8 016. will 8 017. be 7 018. correct 7 019. get 7 020. go 7 021. just 7 022. here 6 023. light 6 024. do 5 025. every 5 026. problem 5 027. they 5 028. write 5 029. clear 4 030. how 4 031. learn 4 032. loud 4 033. say 4

Unique to second 241 tokens

95 families

Freq first (then alpha) 001. always 20 002. where 9 003. beauty 8 004. oh 8 005. song 7

006. you’re 7

007. from 6 008. home 6 009. life 6 010. ready 6 011. back 5 012. alone 4 013. hand 4

014. home” 4

015. line 4 016. mile 4 017. never 4 018. sing 4 019. we 4

020. “son 4

021. far 3 022. help 3 023. horizon 3 024. love 3 025. million 3 026. people 3 027. there 3 028. absence 2 029. child 2 030. dark 2 031. deep 2

VP novel items

Same list Alpha first 001. absence 2 002. alone 4 003. also 1 004. always 20 005. ask 1 006. back 5

007. back…back…

1

008. beauty 8 009. because 1 010. belong 1 011. blank 1 012. bright 1 013. child 2 014. dark 2 015. deep 2

016. don’ 2

017. download 1 018. easy 1 019. eye 1

020. eyes… 1

021. fact 1 022. far 3 023. father 2

024. fathers’ 1

025. fill 1 026. from 6 027. glory 1 028. guitar 1 029. hand 4

58 031. fruit 9

032. lid 9 033. minute 9 034. novel 9 035. raise 9 036. self 9 037. close 8 038. handwrite 8

039. measure 8 040. method 8 041. pudding 8 042. basket 7 043. put 7 044. syrup 7 045. table 7 046. vegetable 7

047. warm 7 048. between 6 049. body 6 050. bowl 6 051. cream 6 052. food 6 053. mixture 6 054. peel 6 055. plug 6 056. rinse 6 057. shred 6 058. sift 6 059. slice 6 060. step 6

061. • 6

062. difference 5

063. dish 5 064. dress 5 065. english 5 066. green 5 067. stir 5 068. study 5 069. two 5 070. until 5 071. up 5 072. by 4 073. consist 4 074. core 4 075. fingertip 4

076. five 4 077. gram 4 078. heat 4 079. heatproof 4

080. immediate

034. some 4 035. tell 4 036. with 4 037. word 4 038. about 3 039. but 3 040. find 3 041. first 3 042. for 3 043. good 3 044. group 3 045. meaning 3 046. over 3 047. right 3 048. use 3 049. when 3 050. active 2 051. any 2 052. class 2 053. copy 2 054.

dictionary 2 055. he 2 056. into 2 057. journal 2 058. like 2 059. look 2 060. make 2 061. more 2 062. most 2 063. necessary 2

064. no 2 065. on 2 066. or 2 067. part 2 068. person 2 069. same 2 070. sentence 2

071. spell 2 072. sure 2 073. then 2 074. together 2

075. while 2 076. work 2 077. after 1 078. another 1 079. at 1 080. below 1 081. best 1 082. book 1 083. care 1 084. chapter 1

032. don’ 2

033. father 2 034. happy 2 035. hold 2 036. inside 2 037. interest 2 038. irrefutable 2

039. may 2 040. mother 2 041. night 2 042.

ohh…ohh…ohh… 2 043. parent 2 044. protect 2 045. road 2 046. see 2 047. seem 2 048. she 2 049. slippery 2 050. slope 2 051. sun 2

052. telescope 2 053. through 2 054. worry 2

055. ‘cause 2

056. also 1 057. ask 1

058. back…back…

1

059. because 1 060. belong 1 061. blank 1 062. bright 1 063. download 1 064. easy 1 065. eye 1

066. eyes… 1

067. fact 1

068. fathers’ 1

069. fill 1 070. glory 1 071. guitar 1 072. instrument 1

073. lyric 1 074. many 1 075. message 1 076. moon 1

077. mothers’ 1

078. music 1 079. nature 1 080. open 1 081. popular 1 082. possible 1

030. happy 2 031. help 3 032. hold 2 033. home 6

034. home” 4

035. horizon 3 036. inside 2 037. instrument 1

038. interest 2 039. irrefutable 2

040. life 6 041. line 4 042. love 3 043. lyric 1 044. many 1 045. may 2 046. message 1 047. mile 4 048. million 3 049. moon 1 050. mother 2

051. mothers’ 1

052. music 1 053. nature 1 054. never 4 055. night 2 056. oh 8 057.

ohh…ohh…ohh… 2 058. open 1 059. parent 2 060. people 3 061. popular 1 062. possible 1 063. protect 2 064. ready 6 065. return 1 066. road 2 067. see 2 068. seem 2 069. share 1 070. she 2 071. sing 4 072. sky 1 073. slippery 2 074. slope 2 075. son 1 076. song 7 077. space 1 078. sun 2 079. talk 1 080. teach 1 081. telescope 2

59 4

081. knot 4 082. lengthwise 4

083. lump 4 084. margarine 4

085. move 4 086. order 4 087. practise 4 088. rub 4 089. rule 4 090. salad 4 091. scale 4 092. scoop 4 093. should 4 094. thick 4 095. thing 4 096. thorough 4 097. tie 4 098. turn 4 099. underline 4

100. as 3 101. bake 3 102. centimetre 3

103. complete 3 104. contain 3 105. cool 3 106. cut 3 107. firm 3 108. four 3 109. goal 3 110. gold 3 111. handle 3 112. hour 3 113. hundred 3 114. level 3 115. mix 3 116. paper 3 117. piece 3 118. pour 3 119. press 3 120. proper 3 121. recommend 3

122. rest 3 123. room 3 124. speak 3 125. spice 3 126. start 3 127. taste 3 128. teaspoon 3 129. title 3

085. difficult 1

086. example 1 087. front 1 088. less 1 089. let 1 090. listen 1 091. long 1 092. mark 1 093. need 1 094. not 1 095. note 1 096. one 1 097. present 1 098. punctuate 1

099. read 1 100. reflect 1 101. repeat 1 102. state 1 103. time 1 104. too 1 105. what 1

083. return 1 084. share 1 085. sky 1 086. son 1 087. space 1 088. talk 1 089. teach 1 090. thousand 1 091. very 1 092. view 1 093. way 1

094. we’ve 1

095. youtube 1

082. there 3 083. thousand 1 084. through 2 085. very 1 086. view 1 087. way 1 088. we 4

089. we’ve 1

090. where 9 091. worry 2 092. youtube 1

093. you’re 7

094. ‘cause 2

60 130. young 3

131. “cook” 3

132. “warm” 3

133. accident 2 134. adjust 2 135. all 2 136. avocado 2 137. bean 2 138. before 2 139. boil 2 140. butter 2 141. certain 2 142. closes 2 143. combine 2 144. cord 2 145. crush 2 146. cube 2 147. cubed 2 148. damage 2 149. design 2 150. dissolve 2 151. drink 2 152. entitle 2 153. etc 2 154. few 2 155. final 2 156. full 2 157. graduate 2 158. grate 2 159. half 2 160. hear 2 161. inform 2 162. jackfruit 2

163. keep 2 164. large 2 165. leaf 2 166. little 2 167. low 2 168. manner 2 169. mistake 2 170. out 2 171. power 2 172. prepare 2 173. quarter 2 174. require 2 175. ripe 2 176. saucepan 2 177. section 2 178. settle 2 179. show 2 180. slight 2 181. small 2 182. soak 2 183. states 2

61 184. structure

2

185. tablespoon 2

186.

temperature 2 187. therefore 2

188. unit 2 189. well 2 190. according 1

191. achieve 1 192. appliance 1

193. audience 1 194. automatic 1

195. avoid 1 196. banana 1 197. both 1 198. box 1 199. capacity 1 200. cassava 1 201. caster 1 202. celsius 1 203. chilies 1 204. choose 1 205. chop 1 206. clove 1 207. coarse 1 208.

conjunction 1 209. coriander 1

210. degree 1 211. deseeded 1 212. dessert 1 213. divide 1 214. drain 1 215. drizzle 1 216. efficient 1

217. eight 1 218. evaporate 1

219. except 1 220. fine 1 221. finish 1 222. follow 1 223. fresh 1 224. garlic 1 225. generous 1 226. give 1 227. ground 1

62 228. harm 1

229. kind 1 230. knob 1 231. lead 1 232. length 1 233. lime 1 234. litre 1 235. machine 1 236. material 1 237. maximum 1 238. meat 1 239. mention 1 240. ml 1 241. model 1 242. next 1 243. object 1 244. operate 1 245. option 1 246. overcook 1 247. own 1 248. palm 1 249. parts: 1 250. perform 1 251. pie 1 252. plan 1 253. prevent 1 254. proceed 1 255. process 1 256. red 1 257. result 1 258. salt 1 259. scrape 1 260. separate 1 261. size 1 262. so 1 263. spinach 1 264. spoon 1 265. sprout 1 266. steamer 1 267. sweet 1 268. text 1 269. than 1 270. tip 1 271. tool 1 272. top 1 273. toss 1 274. vary 1 275. waste 1 276. wrap 1 277. yet 1

63 Unique to first

797 tokens 279 families

001. direction 29 002. product 25 003. label 22 004. drug 21 005. date 15 006. store 15 007. tea 15 008. dose 13 009. inform 13 010. expire 12 011. pack 12 012. content 11 013. free 11 014. amount 10 015. available 10 016. describe 10 017. ingredient 10

018. keep 9 019. analyse 8 020. contain 8 021. guide 8 022. oil 8 023. olive 8 024. oral 8 025. cough 7 026. yes 7 027. bag 6 028. by 6 029. four 6 030. name 6

031. • 6

032. as 5 033. flour 5 034. greece 5 035. medicine 5 036. oregano 5 037. out 5 038. pain 5 039. seven 5 040. before 4 041. cup 4 042. direct 4 043. each 4 044. fever 4 045. five 4 046. food 4

047. mililitres 4 048. only 4

Shared 503 tokens 100 families

001. you 68 002. the 36 003. i 26 004. a 23 005. it 19 006. to 17 007. in 14 008. this 13 009. and 12 010. come 12 011. of 10 012. if 9 013. have 8 014. know 8 015. will 8 016. be 7 017. correct 7 018. get 7 019. go 7 020. just 7 021. from 6 022. here 6 023. do 5 024. every 5 025. problem 5 026. they 5 027. write 5 028. clear 4 029. hand 4 030. how 4 031. learn 4 032. loud 4 033. say 4 034. we 4 035. with 4 036. word 4 037. about 3 038. but 3 039. find 3 040. first 3 041. for 3 042. group 3 043. meaning 3 044. over 3 045. there 3 046. use 3 047. when 3 048. active 2 049. any 2

Unique to second 262 tokens

100 families

Freq first (then alpha) 001. always 20 002. can 14 003. where 9 004. beauty 8 005. oh 8 006. song 7

007. you’re 7

008. home 6 009. life 6 010. light 6 011. ready 6 012. back 5 013. alone 4

014. home” 4

015. line 4 016. mile 4 017. never 4 018. sing 4 019. some 4 020. tell 4

021. “son 4

022. far 3 023. good 3 024. help 3 025. horizon 3 026. love 3 027. million 3 028. people 3 029. right 3 030. absence 2 031. dark 2 032. deep 2

033. don’ 2

034. father 2 035. he 2 036. hold 2 037. inside 2 038. interest 2 039. into 2 040. irrefutable 2

041. mother 2 042. necessary 2 043. night 2 044.

ohh…ohh…ohh… 2 045. parent 2

VP novel items

Same list Alpha first 001. absence 2 002. alone 4 003. always 20 004. another 1 005. ask 1 006. back 5

007. back…back…

1

008. beauty 8 009. because 1 010. belong 1 011. blank 1 012. book 1 013. bright 1 014. can 14 015. dark 2 016. deep 2

017. don’ 2

018. download 1 019. easy 1 020. eye 1

021. eyes… 1

022. far 3 023. father 2

024. fathers’ 1

025. fill 1 026. glory 1 027. good 3 028. guitar 1 029. he 2 030. help 3 031. hold 2 032. home 6

033. home” 4

034. horizon 3 035. inside 2 036. instrument 1

037. interest 2 038. into 2 039. irrefutable 2

040. let 1 041. life 6 042. light 6 043. line 4 044. listen 1

64

049. paper 4 050. piece 4 051. practise 4 052. sugar 4 053. syrup 4 054. three 4 055. two 4 056. : 3

057. alcohol 3 058. aspirin 3 059. away 3

060. difference 3 061. drink 3 062. english 3 063. formula 3 064. fresh 3 065. identify 3 066. liquid 3 067. mixture 3 068. now 3

069. nutrition 3 070. other 3 071. overdose 3 072. pound 3 073. reach 3 074. reducer 3 075. reliever 3 076. start 3 077. table 3 078. task 3 079. teaspoon 3 080. temperature 3

081. than 3 082. twenty 3 083. water 3 084. wheat 3 085. year 3 086. according 2 087.

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