Methodologies for Researching Cultural Diversity in Education: International Perspectives
Bibliographic record
Abstract
As teachers, education policymakers and school managers seek to meet the needs of students from cultures and language backgrounds different from the dominant majority's, research needs to reflect the perspectives of the students themselves and of their parents and teachers, while taking account of the broader socio-political context. This book brings together research conducted in Scotland, Australia, Canada, Norway, Italy, Ghana and Pakistan, which addresses the ethical conduct of education research in culturally and linguistically diverse contexts. The relationship between researched and researcher is crucial, but it can be problematic when the researchers are from the dominant group and not the groups whose experiences they aspire to understand. These authors highlight the challenges of researching in culturally and ethnically diverse contexts, and describe innovative approaches such a mapping, shadowing and photography that give agency to the children who are being researched, rather than to the researchers. The book is of interest to academics and to classroom teachers researching their own practice, and also to education students and social science researchers working in culturally diverse contexts.
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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 | 0.030 | 0.015 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.007 | 0.013 |
| Science and technology studies | 0.005 | 0.024 |
| Scholarly communication | 0.019 | 0.019 |
| Open science | 0.002 | 0.011 |
| Research integrity | 0.003 | 0.007 |
| Insufficient payload (model declined to judge) | 0.005 | 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".