European Union developments in Equality and Human Rights: The Impact of Brexit on the divergence of rights and best practice on the island of Ireland
Bibliographic record
Abstract
This report was commissioned by the Equality Commission for Northern Ireland (ECNI) further to its role, with the Northern Ireland Human Rights Commission (NIHRC), as the Dedicated Mechanism under Article 2(1) of the Ireland/Northern Ireland Protocol. This paper was written by Sarah Craig, Anurag Deb, Eleni Frantziou, Alexander Horne, Colin Murray, Clare Rice and Jane Rooney for the Equality Commission for Northern Ireland, the Northern Ireland Human Rights Commission and the Irish Human Rights and Equality Commission. The views expressed within this paper are those of the authors and do not necessarily represent the views of the Equality Commission for Northern Ireland, the Northern Ireland Human Rights Commission or the Irish Human Rights and Equality Commission, nor the employers of the authors. Responsibility for any statements, errors or omissions in this reportrests with the authors. This paper is not intended to be relied upon as legal advice applicable to any individual case.
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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 | 0.006 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.004 | 0.016 |
| Scholarly communication | 0.014 | 0.006 |
| Open science | 0.001 | 0.007 |
| Research integrity | 0.002 | 0.005 |
| Insufficient payload (model declined to judge) | 0.014 | 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".