BRIDGING THE DIVIDE? Integrating the Functions of National Equality Bodies and National Human Rights Institutions in the European Union
Bibliographic record
Abstract
This study concentrates upon developments in five EU member states, where integrated bodies have been or are in the course of being established: Belgium, Denmark, Ireland, the Netherlands and the UK (with specific reference to Britain). Five in-depth expert reports were prepared for this study, which analysed how the integration process has proceeded in each of hese states: all of these reports were based on data collected through a combination of deskbased research and interviews with key stakeholders from civil society, government departments, academia and the institutions directly affected by the integration process. This study also refers to the recent integration of the French NEB - Haute Autorité de Luttecontre les Discriminations et pour l’Egalité (HALDE) - within the institution of the Défenseur des Droits in France, which was the subject of a sixth in-depth expert report, and to the experience of the Polish Ombudsman in combining the functions of a NEB and a NHRI as extensively documented in academic and official publications. Reference is also made to developments in other EU states and non-EU states such as Australia and Canada whereappropriate, and to the results of further desk-based research and interviews with representatives of the European Network of Equality Bodies (Equinet), the European Network of Human Rights Institutions, the EU Agency for Fundamental Rights (FRA), the UN Office for the High Commissioner on Human Rights (OHCHR) and the European Commission.
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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 | 0.031 | 0.018 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.009 | 0.025 |
| Scholarly communication | 0.025 | 0.028 |
| Open science | 0.002 | 0.015 |
| Research integrity | 0.005 | 0.005 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".