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Record W4403323387 · doi:10.5539/ijel.v14n5p94

Grammatical Cohesion in the Introduction Chapters of Linguistics Ph.D. Theses Written by Anglophone Academic Writers

2024· article· en· W4403323387 on OpenAlexvenueno aff
Laila Abdullah AlSuwaiyan

Bibliographic record

VenueInternational Journal of English Linguistics · 2024
Typearticle
Languageen
FieldArts and Humanities
TopicDiscourse Analysis in Language Studies
Canadian institutionsnot available
Fundersnot available
KeywordsCohesion (chemistry)LinguisticsApplied linguisticsAcademic writingPsychologyPhilosophyChemistry

Abstract

fetched live from OpenAlex

Effective formal writing skills are crucial for educational success and improved academic status; however, writing in English can be challenging, especially for inexperienced English writers and for those individuals for whom English is not their primary language. One often experienced challenge is the appropriate use of cohesive devices (CDs) to organize texts and enhance their semantic potential. Thus, this study analyzed the grammatical CDs utilized by Anglophone Academic Writers (AAWs) in the introduction chapters of 45 Ph.D. theses (86,000 words) in the field of linguistics using Halliday and Hasan’s CDs taxonomy. The analysis showed that AAWs mainly employed references, particularly personal and demonstrative, followed by conjunctions, particularly additive ones, as their dominant CDs. Additive conjunctions helped establish the coherent progressions of ideas in the introductions, while personal references, especially first-person pronouns, conveyed authorial presence. Demonstrative references emphasized important ideas discussed in the theses. The strategic application of these grammatical CDs effectively directed readers through the introductions, creating a logical flow for the writers’ points. Therefore, this study presents valuable insights into the cohesive writing practices of AAWs, which can be used to enhance writing instruction and help novice English writers in improving their formal writing skills.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.047
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.820
Threshold uncertainty score0.962

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.047
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0000.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.021
GPT teacher head0.297
Teacher spread0.276 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

Study designNot applicable
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations0
Published2024
Admission routes1
Has abstractyes

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