Issues and Impacts: The Irish Travelling and Roma Communities
Bibliographic record
Abstract
Research on the experiences of Gypsy, Romany and Traveller prisoners reported that their numbers ‘[continue] to be underestimated within the custodial estate’ and that ‘the distinct needs of this group are often not recognised and go unsupported’ (HMIP, 2014: 11). This chapter reviews the experience of prison lockdowns during the COVID-19 pandemic on the Irish Travelling and Roma communities. As traditionally nomadic and significantly marginalized communities, we propose that they may have faced unique challenges in relation to isolation, contacting their families, health inequalities and barriers in accessing culturally appropriate supports. The Irish Travelling community, often referred to as Travellers or Mincéirí, are a minority ethnic group, distinct from the Irish majority population due to differing cultural and traditional attributes, including strong family structures, unique language (Shelta), staunch religiosity and a history of nomadism (Hayes, 2006; Mulcahy, 2012; Bracken, 2014; 2016; Department of Justice and Equality, 2017; Gavin, 2019). Though indigenous to Ireland, Irish Travellers now reside in a number of countries, including the United States and Canada. However, the largest population of Irish Travellers outside of Ireland is in the United Kingdom (Mac Gabhann, 2011). In the Republic of Ireland, the latest census reported that the Irish Travelling community comprised 0.7 per cent of the total population – approximately 31,000 individuals (Central Statistics Office, 2017a). In the United Kingdom, approximately 63,000 people identified themselves as Gypsy, Traveller or Irish Traveller in the latest census.
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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 | 0.004 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.026 | 0.014 |
| Scholarly communication | 0.012 | 0.008 |
| Open science | 0.003 | 0.021 |
| Research integrity | 0.004 | 0.008 |
| Insufficient payload (model declined to judge) | 0.017 | 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".