Bibliographic record
Abstract
This article uses computer learner corpora to compare the variety and frequency of some modal expressions in the writing of university-level EFL students and native speakers. Even though the prime focal point of the investigation is Brazilian EFL writers, the author recurrently relies on comparisons with Spanish and Czech EFL writers in an effort to determine whether certain characteristics of Brazilian EFL writing are likely to stem from mother tongue interference, or are, by and large, shared by EFL writers of different language settings. The study is based on four 33,000-word sub-corpora and takes into account not only modal auxiliary verbs but also a broader variety of modal devices, such as lexical verbs and adverbs with a modal value. Results reveal an overall overuse of modal expressions by all EFL writers, a propensity which may be partially developmental, and partially interlingual. The study also discovers evidence of register interference, where the learners appear to transfer patterns of use from spoken English into their writing, and particularly a high-degree of topic sensitivity in the use of particular modals. It concludes by arguing for the necessity to offer learners a broader variety of modal expressions including larger sentence patterns and modal phrases.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.005 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.821 | 0.729 |
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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".