Building Bilingual Education Systems: Forces, Mechanisms and Counterweights
Bibliographic record
Abstract
Part 1. Looking at the big picture: 1. United States of America - the paradoxes and possibilities of bilingual education 2. Europe - supra-national interventions promoting bilingual education 3. Canada - factors that shaped the creation and development of immersion education 4. Estonia - laying the groundwork for bilingual education 5. Utah - making immersion mainstream 6. Basque Country - plurilingual education 7. Kazakhstan - from twenty trilingual schools to thousands? Voices from the field - England Voices from the field - Canada Voices from the field - Spain Part 2. Looking at the long-term 8. Cymru/Wales - towards a national strategy 9. The Netherlands - quality control as a driving force in bilingual education Voices from the field - Aotearoa/New Zealand Voices from the field - Cymru/Wales Part 3. Understanding the Role of Context 10. United Arab Emirates - searching for an elusive balance in bilingual education 11 Malta - bilingual education for self-preservation and global fitness 12. Colombia - challenges and constraints 13. South Africa - three periods of bilingual or multilingual education Voices from the field - Brunei Conclusion Forces, Mechanisms and Counterweights Appendix: tools introduction Tool 1. National or regional-level planning considerations for bi-/trilingual education Tool 2. A bilingual education continuum.
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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 | 0.004 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.004 | 0.009 |
| Scholarly communication | 0.009 | 0.006 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.019 | 0.003 |
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".