Non-recent Institutional Abuses and Inquiries: Truth, Acknowledgement, Accountability and Procedural Justice
Bibliographic record
Abstract
Over the last two decades, historical abuse in state and religiously-operated institutions and some civil society groups and organisations has come under scrutiny around the world. The island of Ireland, comprising Northern Ireland and the Republic of Ireland, has had a large number of investigations, redress schemes or apologies regarding non-recent institutional abuse against women and children, some of which are ongoing. Many of these efforts have been criticised by victims/survivors, academic activists and advocates for deficient processes or inadequate recommendations or outcomes. Despite widespread acknowledgement that recent official responses to non-recent institutional abuse are lacking in terms of their capacity to deliver truth, acknowledgement, accountability, and procedural justice, discourses are rarely informed by detailed empirical assessment of the views of key stakeholders including victims/survivors, victim-advocates/representatives, lawyers and human rights advocates, judges/commissioners, politicians, policymakers and members of churches and religious orders. This is an important moment, therefore, to stand back and assess justice responses to non-recent institutional abuse across the island of Ireland and how they compare to efforts across the world. This research will provide a guiding standard to improve social and public understanding to redress non-recent institutional abuse across Ireland and elsewhere.
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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 | 0.026 | 0.053 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.008 | 0.027 |
| Scholarly communication | 0.012 | 0.009 |
| Open science | 0.001 | 0.008 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.009 | 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".