Coherence Relations and Niche Establishment in Applied Linguistics PhD Thesis Introductions
Bibliographic record
Abstract
This study explores how PhD students of applied linguistics relate sentences to be coherent in the process of realizing a communicative move of the PhD thesis introduction genre called Move 2 or “establishing a niche” (Swales, 1990). The analyzed data includes 300 excerpts extracted from 300 thesis introductions, each consisting of sentences used by the thesis writer to establish their research niche. The “Create A Research Space” model (Swales, 1990) was used to identify sentences used to establish a niche, and rhetorical structure theory (Mann & Thompson, 1988) was used to analyze how these sentences are related to each other in this process. The study sought to identify the types of coherence relations utilized to realize Move 2 and the frequency with which they occur. The findings revealed that the use of coherence relations varied across the different rhetorical steps. In Step 1B (Indicating a gap), Elaboration was the most common, followed by Contrast and Concession. For Step 1C (Question-raising), Elaboration was again the most common, followed by the List relation. In Step 1D (Continuing a tradition), Contrast, Elaboration and Motivation were frequently used. These findings may have implications for teaching academic writing to PhD students and for the ways novice researchers use to situate their work within the existing literature.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.058 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
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".