Investigating Performance of the Third- Person Singular –S as a Learnt Property through Orally Repeated Practice Based on Krashen’s Monitor Conditions of Time and Form Focuses
Bibliographic record
Abstract
This study aims at investigating if the third- person singular as a learnt property can be released from the monitor conditions of time and form focuses as long as the trainees keep practicing it orally. The study also aims to identify if such property can be developed to the automatic extreme of the continuum when the trainees are aware of the rule. To achieve this, 30 trainees who produced conditions- focused and unfocused singular present verbs were identified in certain instances at the beginning of the program for later observation. Accordingly, tape- recordings of the trainees’ oral translations were obtained as they kept practicing and semi- structured interviews were conducted with them along the program. The results showed that the trainees who were checked to say correct forms of the conditions- unfocused singular present verbs were affected more than those who just merely saying the conditions- focused counterparts. The effect was in the form of focusing on the conditions less and less as well as developing their cognitive skills as few trainees followed mentally new mechanisms to say correct forms of the unfocused present verbs. Consequently, it can be said that such trainees reached the automaticity of the knowledge they produced earlier as learnt one.
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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 | 0.002 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".