Critical Human Rights, Citizenship, and Democracy Education
Bibliographic record
Abstract
Critical Human Rights, Citizenship, and Democracy Education presents new scholarly research that views human rights, democracy and citizenship education as a critical project. Written by an international line-up of contributors including academics from Canada, Cyprus, Ireland, South Africa, Sweden, the UK and the USA, this open access book provides a cross-section of theoretical work as well as case studies on the challenges and possibilities of bringing together notions of human rights, democracy and citizenship in education. The contributors cultivate a critical view of human rights, democracy and citizenship and revisit these categories to advance socially just educational praxis and highlight ground-breaking case studies that redefine the purposes and approaches in education for a better alignment with the justice-oriented objectives of human rights, democracy and citizenship education. A critical response, reflecting on the issues raised throughout the book, provides a conclusion. This is essential reading for those researching these pedagogical forms and will be valuable to practitioners and activists in fields as diverse as education, law, sociology, health sciences and social work and international development. The ebook editions of this book are available open access under a CC BY-NC-ND 4.0 licence on bloomsburycollections.com. Open access was funded by Knowledge Unlatched.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.004 | 0.019 |
| Scholarly communication | 0.010 | 0.006 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.011 | 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".