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Author: 徐筱玲
Hsu, Hsiao-Ling
Thesis Title: 字串之結構與功能於跨領域學術演講之研究
A Study on the Structures and Functions of Lexical Bundles Used in Cross-disciplinary Academic Lectures
Advisor: 陳浩然
Chen, Hao-Jan
Degree: 博士
Doctor
Department: 英語學系
Department of English
Thesis Publication Year: 2017
Academic Year: 105
Language: 英文
Number of pages: 391
Keywords (in Chinese): 學術英文片語學語料庫語言學字串學術演講
Keywords (in English): EAP, Phraseology, Corpus linguistics, lexical bundles, academic lectures
DOI URL: https://doi.org/10.6345/NTNU202202641
Thesis Type: Academic thesis/ dissertation
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  • 利用語料庫工具研究字串(Lexical bundles),在文獻中有許多的新發現,包含字串結構和功能在學術寫作上抑或是一般日常口語上的研究成果。但是,字串在學術口語上的研究卻相對地有限。所以本研究將探討學術演講中字串的結構和功能,並且進一步地探討其跨領域間的不同。
    為了建立學術演講的語料庫,透過網際網路上的搜尋,共找出八個網站提供線上免費課程,和課程的逐字稿。基於可取得學術演講的口語資料,本研究的語料庫包含五個領域(工程、物理科學、生物和健康科學、社會科學和教育、和人文),每個領域分別含有三個子領域。每個領域約有1百萬字,語料庫總共約6百萬字。本語料庫資料利用WordSmith (Scott, 2016)語料庫工具搜尋出頻率超過30次、且在同一個領域中須出現於五個不同講者的逐字稿中的四字字串。這些字串將進一步地藉由兩個調整過的模型,來分析所有字串的結構和功能: Biber et al. (1999)的結構模型和Biber (2006)的功能模型。
    研究結果顯示即使講者來自於不同的領域,他們在字串的結構上有很多相似的使用表現,大部分的講者都用更多人稱代名詞結構(30%)的字串。這個結構常用來展現講者的個人意願或希望的功能,例如,“we’re going to、I would like to”。以講者為主的課堂中,講者利用這兩種功能的字串去傳達知識。這種現象在所有的領域中都相當普遍,除了人文領域。但是,不同領域講者在人稱代名詞結構上,會選擇不同的人稱代名詞。例如、硬領域(Hard field;工程、物理科學、生物和健康科學)的講者傾向於用“we”去包含學生;相對地來說,軟領域(Soft field;社會科學和教育、和人文)的講者多用“you”去指稱學生。這兩種不同人稱代名詞的選擇,展現了不同領域的知識組成之本質。
    除了人稱代名詞結構外,大多數各個領域的講者最常使用的結構為: to子句結構、代名詞結構、和名詞與of短句結構。這三個結構所形成的功能為: 言談和指涉功能。但是,人文領域的講者較其他領域的講者使用更多名詞與of短句結構(例如: the end of the)和介係詞與of結構(例如: at the time of),而這兩種結構所形成的功能多為言談功能。人文領域的講者會利用此種功能做更多的推論式的說明。
    在字串功能性部分的討論,共有三種主要的功能: 表達立場、言談、和指涉功能。表達立場功能的字串幾乎在所有領域中都是比例最高的,其中表達渴望和個人意願的功能是最常使用的子功能。在這兩種功能的字串中,硬領域(特別是工程、和物理科學)的講者使用更多這兩種類型的字串。再加上,字串中會包含更多不同的動詞去引導課室中各式內容,例如I want to do、I’m going to do。
    在言談功能的研究中,有三種主要的功能: 介紹主題、主題解釋、和重點表達的功能。其中以主題解釋的功能上有最多領域之間的差異。例如、硬領域(工程、物理科學、生物和健康科學)的講者使用更多的動作表達、假設、和框架的功能。相對地來說,軟領域(特別是人文)的講者使用更多的比較和對比、關係表達、和框架的功能。
    最後一項是指涉功能,這類型的字串包含時間、地點或屬性描述的功能。這三種類型的功能,在所有領域中都普遍地被使用。但是,在描述屬性的功能字串上有明顯地跨領域之不同。硬領域中的字串會包含專業的字彙,例如: the derivative of the。相對地來說,軟領域中相同類型的字串多為大眾可以理解的內容,例如: the price of the。
    雖然在字串的結構或功能上,各個領域的講者都有相似的用法,其中以表達立場字串為多數領域中比例最高的字串類型。但是,在比較各個領域100個最頻繁的四字字串中發現,不同領域的講者都一定會頻繁地使用言談功能的字串,例如: on the other hand、going to talk about。因此雖然表達立場的字串為多數,但是言談功能字串是學術演講中最頻繁被使用的字串類型。
    透過本研究的結果,可以更進一步地了解字串在學術演講中的結構和功能之分佈,也了解其跨領域之間的異同。藉由本研究的結果,可以幫助非母語人士教師,透過運用不同功能的字串作為課室引導,以期望學生能夠更容易理解教師的課堂內容。也可以幫助EAP教材撰寫者,使他們能夠將不同結構和功能的字串融入到不同領域的學術英文教材中。

    Lexical bundles have become one of the most important topics in linguistic research into academic contexts. While ample research findings reveal how lexical bundles are used in academic writing and in daily conversation, little research has been conducted on how spoken lexical bundles are used in academic contexts. Therefore, this study aims to explore how lexical bundles are employed in academic lectures across disciplines, focusing on both similar and different uses.
    In order to address the research questions proposed in the dissertation, lecture data were collected from eight websites online offering free courses and lecture transcripts. The collected data was organized into five academic disciplines: Engineering, Physical Science, Biology and Health Science, Social Science and Education, and the Humanities. Each academic discipline contained three sub-disciplines and included about one million words, with around six million words in total for the five academic disciplines. The transcripts were put into WordSmith (Scott, 2016) to collect four-word lexical bundles. Lexical bundles that appeared more than 30 times per million words and appeared in more than five different lecturers’ transcripts in one discipline then underwent a structural and a functional analysis using two adapted models: Biber et al.’s (1999) structural model and Biber’s (2006) functional model.
    The results suggest that there are many similarities in the lecturers’ use of lexical bundle structures. From the eighteen structures adapted from Biber’s (1999) structural model, all lecturers use more personal pronoun structure (30%) which is frequently used for two main functions relating to stance expressions: desire (e.g., I would like to) and personal intention (e.g., we’re going to) function. While most of the lecturers commonly use the personal intention structure to deliver content knowledge, they differ in their selection of personal pronouns. While instructors of the hard fields (Engineering, Physical Science, and Biology and Health Science) use more “we” to involve students, instructors of the soft fields (Social Science and Education and the Humanities) tend to use more “you” to direct students’ attention. This is possibly due to the nature of content knowledge in respective disciplines.
    In addition to the personal pronoun structure, to-clause fragments, pronouns, and noun phrases with of-phrase structure are frequently used in most disciplines; however, the Humanities is the exception in its use of more noun phrases with of-phrases (e.g., the end of the) and preposition + of (e.g., at the time of) structures. The two types of structure function mostly as discourse expressions due to the discursive nature of the content knowledge in the Humanities. In other words, the explanation of Humanities knowledge is dependent on the instructors’ interpretation.
    As for functional analysis, most of the instructors use many stance expressions in which the desire and personal intention functions are frequently used. Instructors in the hard fields (especially Engineering and Physical Science) use not only the two types of function more, but also a variety of verbs in lexical bundles, such as I want to do, and I’m going to be.
    In terms of discourse expressions, topic elaboration is one of the functions that displays disciplinary differences. For instance, hard field (Engineering, Physical Science, and Biology and Health Science) instructors use more action, hypothesis, and framing functions, while soft field (Social Science and Education and the Humanities) instructors use more compare and contrast, relationship, and framing functions.
    Finally, referential expressions are mostly used to indicate time, place, or attributes of an entity. Describing the attributes is the biggest difference between the hard and soft fields. While many lexical bundles are incorporated with professional terminologies (e.g., e to the i) in the hard field, the same type of lexical bundles (e.g., the price of the) are generally comprehensible to the general public in the soft fields.
    Both structural and functional analyses of the lexical bundles have shown many similarities across disciplines, with stance expressions being used most frequently. However, when the 100 most frequent four-word lexical bundles were compared, instructors of different disciplines were found to commonly use discoursal lexical bundles, such as on the other hand or going to talk about. In other words, although the majority of lecturers use stance expressions the most, discoursal lexical bundles are an inescapable type of lexical bundle found in lectures.
    These findings have implications for EAP practitioners, non-native English instructors, and students. Students may understand the academic lectures better through the lexical bundles. Non-native English instructors may use the lexical bundles in their lectures to make lectures clearer, while EAP practitioners may incorporate the corpus-informed lexical bundles into EAP listening materials.

    中文摘要 ii Abstract iv Acknowledgement vi Table of Contents vii List of Tables x List of Figures xii Chapter One Introduction 1 1.1 Background 1 1.2 Purpose of the Study 5 1.3 Significance of the Study 7 Chapter Two Literature Review 8 2.1 Definition of Lexical Bundles 8 2.2 Lexical Bundles and the Comparisons between Written and Spoken Modes 14 2.2.1 Studies of lexical bundles used in written and spoken modes 14 2.2.2 Lexical bundles used in an academic spoken activity—lecture 18 2.3 Lexical Bundle Comparisons among Disciplines 23 2.3.1 Studies of disciplinary differences in written discourse 23 2.3.2 Studies of disciplinary differences in spoken discourse 26 Chapter Three Method 31 3.1 Corpora Description 31 3.1.1 The organization of the Lecture corpus 38 3.2 Corpus Tool 41 3.3 Data Analysis and Procedures 44 Chapter Four Results and Discussion 58 4.1 Structural Distribution of Lexical Bundles 58 4.1.1 Comparison across Five Academic Disciplines 58 4.1.2 Engineering 76 4.1.3 Physical Science 86 4.1.4 Biology and Health Science 94 4.1.5 Social Science and Education 101 4.1.6 Humanities 110 4.2 Functional Distribution of Lexical Bundles 118 4.2.1 Two Types of Lexical Bundles Comparison 119 4.2.1.1 Comparison among five academic disciplines. 119 4.2.1.2 Comparison of the 100 most frequent lexical bundles. 141 4.2.2 Engineering 158 4.2.3 Physical Science 181 4.2.4 Biology and Health Science 198 4.2.5 Social Science and education 212 4.2.6 Humanities 225 Chapter Five Conclusion 235 5.1 Summary of the Study 235 5.1.1 Structural analysis 235 5.1.2 Functional analysis 237 5.1.3 100 most frequent lexical bundles analysis 240 5.2 Pedagogical Implications 242 5.3 Suggestion for Future Research 275 5.4 Limitation 276 References 278 Appendix A List of Disciplines in OpenYale 286 Appendix B 100 Most Frequent Lexical Bundles Compared between Hard and Soft Fields 287 Appendix C 100 Most Frequent Lexical Bundles Compared among Five Academic Disciplines 290 Appendix D 50 Most Frequent Lexical Bundles Compared among Disciplines 295 Appendix E Specification of Attributes Lexical Bundles of Five Academic Disciplines 302 Appendix F Functional Distributions of Four Academic Disciplines 304 Appendix G Move Structures of Four Academic Disciplines 321

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