The capability of national education systems to address ethnic diversity : original research
Bibliographic record
Abstract
Modern societies have become much more complex in recent decades, also in terms of ethnic identities and differences. The question arose whether education systems were capable of addressing the needs of ethnic and other minorities in countries across the globe. After examining a cross-section of education systems (in Australia, Canada, China, Israel, Malaysia, Rwanda, Russia and South Africa) with the aid of a set of specially developed criteria, it was concluded that these systems seemed to comply with the criteria in various ways, albeit in different measures and in several configurations. It is recommended that policy makers apply such criteria for enhancing the capability of an education system to address the needs of ethnic minorities and to meet the demands of increased social complexity. Die vermoe van nasionale onderwysstelsels om etniese diversiteit te akkommodeer Samelewings het onlangs toenemend kompleks begin raak, ook in terme van etniese identiteite en verskille. Die vraag is of onderwysstelsels van lande reg oor die wereld in staat is om te voldoen aan die eise van die verskillende etniese en ander minderhede. Na bestudering van 'n steekproef onderwysstelsels (die van Australie, Kanada, Sjina, Israel, Maleisie, Rwanda, Rusland en Suid-Afrika) aan die hand van 'n stel spesiaal ontwerpte kriteria is tot die slotsom gekom dat hierdie stelsels oor die algemeen aan die kriteria voldoen, dog in wisselende mate en in allerlei konfigurasies. Dit is aanbeveel dat beleidmakers kriteria soos hierdie toepas ten einde toe te sien dat 'n onderwysstelsel inderdaad in staat sal wees om te kan voorsien in die behoeftes van etniese minderhede en aan die eise van toenemende sosiale kompleksiteit.
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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 | 0.019 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.006 |
| Science and technology studies | 0.002 | 0.004 |
| Scholarly communication | 0.006 | 0.006 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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 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".