From Sympathy to Reparation for Female Victims of Sexual Violence in Armed Conflicts
Bibliographic record
Abstract
Abstract There is a newfound momentum in international law for reparation for the victims of gross violations of human rights. This momentum has been largely hortative in resonance than actual. The slow progress in translating that desire into tangible, effective reparation programmes is partly attributable to the absence of coherent theoretical bases – especially palatable ones – for reparation in particular cases. It is submitted, however, that in canvassing the theories of reparation, the driving consideration must always remain the interests of victims and not the intellectual satisfaction of knowledgeable and well-meaning experts. The most erudite rationalization of the idea of reparation will be of no consequence if it does not, in practice, assist in improving the lives of the victims. While, it is important always to keep in view the fault-based theories of reparation, it is also advisable to consider the utility of employing the no-fault-based rationale for achieving the aim of reparation when the party at fault is either unavailable or unable to make reparation at all or in full. Hence, guidance might be had to the gratis model of reparation employed in many domestic jurisdictions to make some compensation to victims of violent crimes.
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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 | 0.002 | 0.027 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.005 | 0.004 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.013 | 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".