From policy to practice: empowering minority language speakers in New Zealand
Bibliographic record
Abstract
AThis book explores how researchers in different parts of the world deal with cultural diversity in teacher education programmes. In the nine countries presented in this volume (USA, Scotland, Japan, Finland, Colombia, France, New Zealand, Canada, and Mexico) the discussion centres on how individual teachers can be empowered to become difference-making professionals. This tri-lingual publication sets out to challenge current language ideologies and provide examples of enhancing possibilities. My chapter offered an opportunity to present a lens on New Zealand’s cultural and linguistic landscape, and describe evidence-based pedagogical current practices that in a global sense are leading edge. It combined an historical backdrop with school examples derived from my research-based work in schools. Contributors were by invitation only, most of who were co-presenters at a symposium at the AILA Congress in Essen in 2009. Subsequently, editors have invited me to propose further research and publishing collaborations.
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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 | 0.011 | 0.017 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.009 | 0.012 |
| Scholarly communication | 0.012 | 0.009 |
| Open science | 0.001 | 0.007 |
| Research integrity | 0.005 | 0.004 |
| Insufficient payload (model declined to judge) | 0.007 | 0.001 |
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".