The Study of Interaction in SLA
Bibliographic record
Abstract
This study was conducted to broaden the scope of studies on interaction. It examined the role of interaction in terms of linguistic, affective, and social aspects. A questionnaire was administered and intensive interviews conducted to reveal the reality of communication between Chinese ESL students and Canadian native English speakers and how students made sense of their interaction with native speakers. Some linguistic benefits of interaction were found. A dynamic relationship between anxiety/confidence and interaction was identified. However, a significant gap between students ’ attitude/desire and their interaction intensity was exposed, which was explained by social support and other factors. Cette recherche avait comme objectif d’élargir l’envergure des études sur l’inter-action. Elle a porté sur le rôle de l’interaction en termes d’aspects linguistiques, affectifs et sociaux. Un questionnaire et des entrevues intensives ont révélé la réalité de la communication entre les étudiants chinois en ALS et les Canadiens locuteurs natifs d’anglais, et ont fourni des données sur la perspective des étu-diants quant à leur interaction avec les locuteurs natifs. Nous avons identifié quelques avantages linguistiques découlant de l’interaction, ainsi qu’un rapport dynamique entre l’anxiété/la confiance et l’interaction. Toutefois, un écart signi-ficatif entre l’attitude/le désir des étudiants et l’intensité de leur interaction s’est révélé; celui-ci s’explique par l’apppui social, entre autres facteurs.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.005 | 0.007 |
| Scholarly communication | 0.007 | 0.005 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.008 | 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".