Exploring Race Hate Crime Reporting in Wales Following Brexit
Bibliographic record
Abstract
Most countries consist of many diverse races and cultures, based on historical political decisions, wars or economic changes. Throughout Europe over the past decades the policy of free movement for work as part of the EU agreements has encouraged this activity. Indeed this has been a fundamental idea behind the European Union ever since its inception. However, what can the consequences be for those individuals who, encouraged by such policies, find themselves located in a country which has decided to no longer be part of that system? In particular what impact does this decision appear to have on the way those considered to be “racially different” are treated by others? This article explores the impact the recent decision by Great Britain took to leave the EU (so called Brexit) and its impact upon the number of racially recorded hate crimes in Wales. Using examples from terrorist incidents in Europe, along with the Brexit result, as examples, it provides clear evidence that when certain incidents occur in wider society, there is an impact upon the way in which so called non-indigenous people are treated, which results in an increase in criminality. These results will have resonance for other countries with a mixed population, as well as having implications for those agencies involved in the protection and safety of all inhabitants in their country.
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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 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.004 | 0.005 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".