English as a Second Language in the Mainstream
Bibliographic record
Abstract
Introduction PART 1: AUSTRALIA1. ESL in Australisn schools: from the margins to the mainstreamChris Davison 2. Current policies, programs and practices in school ESLChris Davison 3. Integrating language and content: unresolved issuesChris Davison and Alan Williams 4. Identity and Ideology: the problem of defining and defending ESL-nessChris Davison PART 2: CANADA5. ESL in British ColumbiaMary Ashworth 6. The second language as a medium of learningBernard Mohan 7. Knowledge framework and classroom actionGloria Tang 8. Implementation of the Vancouver School Board's ESL initiativesMargaret Early and Hugh Hooper PART 3: ENGLAND9. England: ESL in the early daysConstant Leung and Charlotte Franson 10. Mainstreaming: ESL as a diffused curriculum concernConstant Leung and Charlotte Franson 11.Evaluation of content-language learning in the mainstream classroomConstant Leung 12. Curriculum identity and professional development: system-wide questionsConstant Leung and Charlotte Franson Conclusion
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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 | 0.000 | 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 | 0.001 | 0.002 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.033 | 0.004 |
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".