Linguistic Ecosystems for Foreign-Language Learning in Canada and Japan: An International Comparison of Where Language-Learning Beliefs Come From
Bibliographic record
Abstract
We report on international research that compares linguistic ecosystems, that is, socially constructed public attitudes and ideologies concerned with foreign-language (FL) learning, in Canada and Japan. Analyzing responses to three interview questions from 125 interviews with five categories of respondent in each country, we suggest that there are a number of key differences between the linguistic ecosystems of the two countries, most notably that whereas Canada appears to promote the learning of foreign culture with little support for FL learning, Japan appears to promote FL learning without the learning of foreign culture.Notre article porte sur la recherche internationale qui compare les écosystèmes linguistiques, c’est à dire les attitudes et idéologies publiques définies par la société et portant sur l’apprentissage d’une langue étrangère, du Canada à ceux du Japon. Une analyse des réponses à trois questions d’entrevue provenant de 125 entrevues avec cinq catégories de répondants de chaque pays nous porte à conclure qu’il existe quelques différences importantes entre les deux écosystèmes linguistiques des deux pays, notamment que le Canada semble promouvoir l’apprentissage d’une culture étrangère tout en fournissant peu d’appui pour l’apprentissage d’une langue étrangère, alors que le Japon semble promouvoir l’apprentissage d’une langue étrangère sans l’apprentissage d’une culture étrangère.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.005 |
| Science and technology studies | 0.010 | 0.005 |
| Scholarly communication | 0.005 | 0.001 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".