An application of second language acquisition research to ESL grammar teaching: What to do with novel passives
Bibliographic record
Abstract
This paper demonstrates how second language acquisition research can inform textbook writers and language teachers. It begins with a summary of research which indicates that inappropriate passives are produced and accepted by learners with a variety of L1s and at different levels of proficiency. Researchers agree that the phenomenon is related to unaccusativity. It then presents an analysis of 40 ESL grammar textbooks which shows that few even mention unaccusative verbs or inappropriate passives in their presentation of active and passive voice. Only 7.5% discuss unaccusative verbs, while 10% give examples of inappropriate passives (explicit mention) and 10% explain that certain verbs cannot passivize (implicit mention). Moreover, those texts which do attempt to deal with unaccusatives and inappropriate passives are not complete, and may mislead the learner. The paper concludes with suggestions for dealing with unaccusativity and inappropriate passives in the ESL classroom, based on the relevant SLA research as well as studies in lexical semantics.
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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 | 0.006 | 0.024 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.002 | 0.007 |
| Scholarly communication | 0.005 | 0.010 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".