The Effects of Topical Knowledge on English Language Writing Performance: A Case of Iranian ESP Economics Students
Bibliographic record
Abstract
The current study investigates the effects of topical knowledge on English language writing performance of 46 Iranian ESP economics students across different levels of English proficiency (elementary, intermediate, and advanced). The participants were asked to write two essays one on about a topic specific to economics and the other pertaining to a general topic. Their writing performances were scored using three components: language, organisation, and content. The overall analyses showed that all the participants performed significantly better on the specific topic. However, compared to intermediate and elementary participants, advanced learners were found to perform significantly better on the two topics in terms of language, organisation, and content. Also, elementary and intermediate learners performed equally on the specific topic with reference to its content. However, intermediate learners were found to perform significantly better than their elementary counterparts in terms of language and organisation. The interviews also showed that due to its familiarity the specific topic was easy for them to write about. The study brings to attention the importance of topical knowledge or prior familiarity with the content of a test in languages for specific purposes (LSP) which influences the learner's performances to a great extent.
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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 | 0.001 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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 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".