Maintenance of L2 English through informal language learning in Québécois young adults
Bibliographic record
Abstract
French-English bilingualism is common in the primarily francophone Canadian province of Quebec, though residents here receive no more second language education than more monolingual populations elsewhere in the country. Past work exploring attitudes toward English among the Québécois has alluded to the fact that their bilingualism might develop through engagement with English media, though no existing literature explores this relationship. The present study sought to measure English proficiency and media use, with the hypothesis that both frequency and depth of engagement with media would predict English vocabulary scores. The Vocabulary Size Test was used to measure English proficiency, and a questionnaire was developed to capture media use. 23 participants, who were recruited through social media, completed the test and questionnaire online. A series of multiple linear regressions revealed no relationship between vocabulary score and frequency of engagement with media. Depth of engagement was found to have a negative relationship with vocabulary in the cases of music listening and of overall media use. These results do not support the hypothesis that there is a positive relationship between engagement with media and English proficiency. Future research could replicate the present study using a larger sample size or different measures or could explore how media might indirectly affect the learning of English in Quebec.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".