Building Bilingual Education Systems: Forces, Mechanisms and Counterweights
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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 it