International perspectives on bilingual education : policy, practice, and controversy
Bibliographic record
Abstract
Foreword (Terrence Wiley, Arizona State University) Introduction SECTION 1: POLICY 1. Language Minority Education in the United States: Power and Policy (John E. Petrovic, The University of Alabama) 2. Language Minority Rights and Educational Policy in Canada (Thomas Ricento and Andreea Cervatiuc, University of Calgary) 3. Education Policy and Language Shift in Guatemala (Ivonne Heinze Balcazar, California State University - Dominguez Hills) SECTION 2: PRACTICE 4. Transitions to Biliteracy: Creating Positive Academic Trajectories for Emerging Bilinguals in the United States (Kathy Escamilla and Susan Hopewell, University of Colorado) 5. Bilingualism and Biliteracy in India: Implications for Education (Prema K. S. Rao, Jayashree C. Shanbal, Sarika Khurana, All India Institute of Speech and Hearing) 6. Making Choices for Sustainable Social Plurilingualism: Some Reflections from the Catalan Language Area (F. Xavier Vila i Moreno, Universitat de Barcelona/Institut d'Estudis Catalans) SECTION 3: CONTROVERSY 7. Reorienting Language-as-Resource (Richard Ruiz, University of Arizona) 8. The Role of Language in Theories of Academic Failure for Linguistic Minorities (Jeff MacSwan and Kellie Rolstad, Arizona State University) 9. A Postliberal Critique of Language Rights: Toward a Politics of Language for a Linguistics of Contact (Christopher Stroud, University of the Western Cape).
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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 | 0.010 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.010 | 0.013 |
| Scholarly communication | 0.015 | 0.009 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.009 | 0.009 |
| Insufficient payload (model declined to judge) | 0.024 | 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".