A Contrastive Study on Generic Structure of Introduction to English Chemistry Research Articles: L1 Chinese Student Writers versus L1 English Published Writers
Bibliographic record
Abstract
<p>Introduction is a significant part-genre of a research article (RA) since it functions to persuade the readership that their research topic has some significance, that there is space for new knowledge around the topic and that the writer can make a contribution to knowledge. Previous studies mainly focused on experienced English L1 and L2 published writers. Masters majoring in Chemical Engineering in China are highly motivated to publish English RAs in international journals due to the requirements for graduation, but to date no work investigates into the disparity between their English RA introductions (ERAIs) and that of high impact SCI journals in terms of generic structure. To fill this gap, drawing on Swales’ CARS model (1990, 2004), this study analyzed the ERAIs written by L1 Chinese masters majoring in chemical engineering and L1 English published writers of the same discipline with a focus on the generic structure. The findings show that the NES published writers employ much more moves than the Chinese student writers, especially Move 1 “establishing a territory” and Move 2 “establishing a niche”. Due to the lack of Move 2, the completeness of the general organization of ERAIs written by the Chinese student writers is much weaker than the NES published writers. From the perspective of the constituent steps, the findings show that the lack of Move 1-Step 3 causes the overall insufficiency of Move 1 in the student writer group (SWG) compared with the published writer group (PWG) and the inadequate Step 1A “indicating a gap” leads to the overall deficiency of Move 2 in SWG. In Move 3, Step 1 is most frequently used in both groups. Although Step 5 and Step 6 are employed in this move but their frequency is low and quite similar to each other in these two groups. The findings have some pedagogical implication on the teaching of writing ERAIs.</p>
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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 | 0.001 | 0.139 |
| 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 | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".