The Effect of Planning on Accuracy in Verbal Agreement in L2 Oral Performance
Bibliographic record
Abstract
Accuracy is widely recognized as one of the main dimensions in evaluating task-based oral performance. Indicators of accuracy vary in the literature of the effects of pre-task planning on the oral production. Enlightened by previous studies in pre-task planning and third-person singular -s, this article intends to find out whether the rate of suppliance of thematic verbal agreement increases as a result of pre-task planning, and whether the rate of suppliance of thematic verbal agreement in third-person singular -s increases as a result of pre-task planning. A case study is carried on one subject who makes oral descriptions of a same graph which shows the number of people at London underground station from 6:00 to 22:00 on two separate occasions. The results of this study show that pre-task planning may have significant effect on accuracy of L2 oral output in thematic verbal agreement as a whole, but it may not affect L2 learners in their use of third-person singular -s.
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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 | 0.008 | 0.084 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".