Redress for victims of enforced disappearances: a comparative perspective
Bibliographic record
Abstract
Enforced disappearance is a multiple and complex human rights violation and a serious international crime. This phenomenon has pervasive implications on individuals and society as a whole, leaving behind a legacy of violence, fear, impunity and overall distrust in the institution of law. The purpose of this thesis is to investigate the human tragedy of enforced disappearance from a range of perspectives in order to address the variety of complications arising from this phenomenon. One such challenge is the provision of redress to victims, a problematic task for international human rights courts. This study will offer a comparative analysis of the remedial jurisprudence of the most prominent regional courts entrusted with the protection of human rights in two different regions affected by the prevalence of enforced disappearance. The originality and core contribution of this thesis lies in the dialogue it establishes between different disciplines in order to gauge the distinct human dimensions affected by this violation, which in turn merit their own appropriate redress. The practice of enforced disappearance strikes at the very identity and dignity of a person, their family and their social infrastructure, and therefore requires a comprehensive and holistic response.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.005 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.015 | 0.020 |
| Scholarly communication | 0.008 | 0.007 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.004 | 0.003 |
| Insufficient payload (model declined to judge) | 0.009 | 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".