Effectiveness Framework for Home-State Non-Judicial Grievance Mechanisms
Bibliographic record
Abstract
After more than fifteen years of public debate, law reform proposals and pressure from international human rights bodies, the Canadian government announced in early 2018 that it intends to create the Canadian Ombudsperson for Responsible Enterprise (CORE). While the CORE mechanism is still in the developmental phase, it is expected that it will have the power to independently investigate allegations of human rights violations against Canadian companies operating abroad in the natural resources and garment sectors. As such, it will be the first home-state non-judicial grievance mechanism of its kind in the world. However, the concept of such a mechanism is not new, but has in fact been the subject of discussion among international human rights institutions for at least ten years, including with significant commentary on the Canadian context. This chapter complies and analyzes existing statements of public international law with respect to these kinds of mechanisms. It then identifies points of consensus and argues that there is an emerging consensus with respect to the principles that such mechanisms should abide by in order to ensure their effectiveness and legitimacy. This analysis will be of significant interest to those in Canada who are working to ensure that the CORE will provide an effective remedy for affected individuals and groups, and to others around the world who hope to create similar mechanisms.
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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 | 0.033 | 0.035 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.006 | 0.002 |
| Science and technology studies | 0.009 | 0.027 |
| Scholarly communication | 0.015 | 0.015 |
| Open science | 0.007 | 0.007 |
| Research integrity | 0.013 | 0.010 |
| Insufficient payload (model declined to judge) | 0.020 | 0.003 |
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".