Bibliographic record
Abstract
In modern society, academic exchanges are mainly processed in the style of English for Academic Purposes. Western countries have long been attaching importance to EAP teaching, while China just begins to teach EAP in the recent decade. With the purpose to enhance the quality of EAP teaching in China, Chinese publishers have published several sets of textbooks on EAP, most of which illustrate the following language features: formality, complexity and objectivity. However, those textbooks seldom discuss the unique writing style of mathematical modeling essays. Despite the fact that these essays are also written in a formal style, complexity and objectivity do not seem to be their focus. In fact, simplicity and subjectivity are considered as the two accepted unique stylistic features of mathematical modeling essays. This leads to the embarrassing result that most of mathematical modeling essays by Chinese authors are written according to the common features of other academic essays. It may deter academic exchanges of Chinese scholars with their counterparts from foreign countries. Therefore, Chinese teachers who teach EAP are required to be aware of the differences between mathematical modeling essays and other common academic essays.
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 machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.006 | 0.040 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.004 |
| Scholarly communication | 0.005 | 0.005 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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 source (direct Gemma or distilled Codex), 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".