Bibliographic record
Abstract
2017年に連邦結成150周年を迎えるカナダは，英語とフランス語を公用語とする二言語国家として知られるが，その実態は複雑であり，必ずしも正確に理解されていない．そこで本稿では，言語景観と二言語話者人口に注目し，カナダの二言語主義の現状と課題を現地調査と国勢調査に基づいて解説する．カナダでは1969年に制定された公用語法により，連邦政府の施設は二言語で表記され，二言語でサービスを提供することになっている．一方で，各州の行政はそれぞれの法令に基づいている．したがって，同じ場所に立地していても，連邦と州の施設では用いられている言語が異なる場合がある．また，英語圏の大都市において二言語話者人口の割合が非都市地域よりもやや高い傾向がみられるものの，ケベック州をはじめとするフランス語を母語とする人口の割合が高い地域において，二言語話者人口の割合が高い傾向はあまり変わっていない．
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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 | 0.003 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.005 | 0.010 |
| Science and technology studies | 0.022 | 0.010 |
| Scholarly communication | 0.008 | 0.003 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.013 | 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".