Experience of multiple disadvantage among Roma, Gypsy and Traveller children in England and Wales
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Roma, Gypsy and Traveller children across Europe experience high levels of disadvantage and have repeatedly been identified as a priority in European Commission policy documents, yet they are often missing or invisible in the large-scale statistical analyses of children at risk of poverty and deprivation that drive policy development and monitoring. In this paper we argue that population Censuses, and other administrative sources, many of which already record Roma ethnicity, are under-utilised as a source of robust and comparable data, allowing the scale, intensity and multi-dimensionality of the challenges facing Roma, Gypsy and Traveller children to be investigated and tracked. We illustrate this through the descriptive analysis of secure microdata from the 2011 Census of England and Wales, which included a pre-coded category for ‘Gypsy or Irish Traveller’ for the first time, and to which we add children identified as Roma. Disadvantage in each of four dimensions - housing, household economic activity, education and health - are examined in turn before computing a multiple deprivation count. Nearly a quarter of Roma, Gypsy and Traveller children in England and Wales aged under 19 are deprived on 3 or more dimensions, compared to just two per cent of other children. And conversely, only a small minority (15%) of Roma, Gypsy and Traveller children are not deprived in any dimension, compared to the majority (67%) of all other children. We conclude that data scarcity should no longer be used as an excuse for a lack of effective policymaking: it is both desirable and feasible to exploit Census data, as a step towards tackling the data deficit, and that the results can improve the design of child poverty and Roma, Gypsy and Traveller integration policies.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.007 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.020 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.000 | 0.002 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it