Roma Poverty and Deprivation: The Need for Multidimensional Anti-Poverty Measures
Bibliographic record
Abstract
Reliable data and robust conceptual framework are two necessary preconditions for anti-poverty measures need to be effective and achieve their goals – bringing people out of poverty. Both preconditions are far from met in the case of Roma – one of the biggest minorities in Europe. Data on the absolute number and distribution of Roma population in the EU is patchy, incomparable – or does not exist at all. Thus addressing the data challenge is a necessary precondition for populating indicators that reflect the true face of Roma poverty – are ultimately, for the efforts to take Roma out of poverty to succeed. In its first part, the paper provides an overview of the available approaches and the possible sources of information that can generate the data necessary for monitoring different aspects of Roma inclusion process. The authors point out that different sources have their strengths and weaknesses and using them in complementary manner is desirable. How to use the data (what indicators to apply) is equally important. In its second part the paper proposes a multidimensional poverty index that is better reflecting the specifics of Roma poverty and exclusion than traditional poverty or vulnerability indicators. However two critically important dimensions remains insufficiently covered – namely ‘agency’ and ‘aspirations’. The authors call for reflecting these dimensions through the thematic components in the standardized European social surveys.
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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 | 0.016 | 0.030 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.005 | 0.007 |
| Science and technology studies | 0.002 | 0.010 |
| Scholarly communication | 0.008 | 0.013 |
| Open science | 0.001 | 0.010 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 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".