Investigating Language-Related Episodes during Mechanical and Meaningful Output Activities
Bibliographic record
Abstract
The present study examines how EFL learners consciously reflect on their language during a set of mechanical and meaningful output activities. Thirty-six Farsi learners of English negotiated on linguistic features and completed six activities over a period of six weeks. The transcripts from the learners’ interaction were analyzed for instances of language-related episodes (LREs), their principal focus on meaning or grammar and their nature and outcome. The results showed that (1) the meaningful output activities elicited significantly more LREs than did the mechanical output activities, (2) while approximately half of the LREs in the meaningful activities focused on lexis and meaning, the majority of LREs in the mechanical activities were directed towards grammatical forms and a small portion was focused on meaning, (3) the two output groups differed significantly in the continuous and correctly solved episodes. The study provides support on the effectiveness of collaborative output activities in pushing learners to verbalize their internal linguistic processing and focusing their attention on a wide range of linguistic features.
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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 | 0.010 |
| 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 | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".