Education in indigenous, nomadic and travelling communities : education as a humanitarian response
Bibliographic record
Abstract
Series Editor's Preface, Colin Brock Introduction: Educational in Indigenous, Nomadic and Travelling Communities: A Global Overview, Rosarii Griffin (University College Cork, Ireland) and Piaras MacEinri (University College Cork, Ireland) 1.Cross-Cultural Communication and Change: Travellers and Roma in the Irish Education System, Dr Mairin Kenny (Education Consultant, Ireland) 2.Education as Cultural Conflict: The Case of English Gypsies in the South of England, Dr Juliet McCaffery (University of Sussex, UK) 3. A Case Study of Gypsy Travellers in the East Riding of Yorkshire, UK, Judith Smith (Minority Ethnic and Traveller Attainment Service (METAS, UK) and Helen Worrell (Family and Early Years Learning Officer, UK) 4.Multi-Dimensional Sami Education: Towards Culture Sensitive Policies, Pigga Keskitalo (Sami University College, Norway), Kaarina Maatta (University of Lapland, Finland) and Satu Uusiautti (University of Lapland, Finland) 5.Roma/Traveller Inclusion in Europe: Why Informal Education is Winning, Christine O'Hanlon (University of East Anglia, UK) 6.Education and 'Orang Asli' in Malaysia: A Country Case Study, Hema Letchamanan (Taylor's University, Malaysia) and Firdaus Ramli (Taylor's University, Malaysia) 7.Indigenous Groups' Education: The Case of North America, Lorenzo Cherubini (Brock University, Canada) 8.Intercultural Bilingual Education, Self-Determination and Indigenous Peoples of the Amazon Basin, Sheila Aikman (University of East Anglia, UK) Index
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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 | 0.005 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.008 | 0.006 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.010 | 0.011 |
| Insufficient payload (model declined to judge) | 0.016 | 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".