The Roma in European Higher Education
Bibliographic record
Abstract
Today, between 10 and 12 million Roma live in Europe, comprising the continent’s largest ethnic minority. However, only 1% participate in higher education. Although the Roma are widely dispersed across Europe, and beyond, they face similar social, political, and economic challenges throughout the continent. A major site of struggle has been access, attendance and achievement in the education sector for Gypsies, Roma and Travellers (GRT). This groundbreaking text explores the Roma in higher education, a topic of great importance since higher education is considered to be a significant pathway out of poverty and to social mobility. Why are participation rates so low? What are the barriers and what are the enablers? This edited collection brings together authors from diverse national and organisational locations including academics, activists and policymakers from Canada, Chile, Finland, Greece, Hungary, Macedonia, Poland, Romania, Serbia, the UK, and the USA. They share and critically analyse contemporary knowledge on research, policies, practices and interventions to promote Roma participation in higher education in a range of European locations. They cover key topics including the representation of Roma communities as living on the margins, but also racism, anti-Gypsyism, Romaphobia, hate crimes and discriminatory practices. The book offers insights into how to fight discrimination and re-distribute higher educational opportunities without objectifying the Roma or representing these rich and diverse communities merely as powerless victims.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.004 |
| Scholarly communication | 0.007 | 0.003 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.011 | 0.002 |
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".