Human Rights Impact Assessment: Trade Agreements and Indigenous Rights
Bibliographic record
Abstract
Finally, in Chapter 12, Caroline Dommen concludes our discussion by addressing how human rights impact assessments can contribute to ensuring that Indigenous rights are upheld in international trade agreements. She considers how explicit reference to the rights of Indigenous peoples, including the UN Declaration on the Rights of Indigenous Peoples, may improve human rights impact assessments as well as trade agreements, from both legal and policy perspectives. There is now a substantial body of impact assessments of actual or likely impacts of trade and investment agreements on human rights, including on the rights of Indigenous peoples. Her chapter describes the role and the objectives of impact assessment, explaining the particular advantages of human rights-based impact assessment. It draws on recommendations of UN human rights mechanisms and analysis of completed impact assessments of trade agreements to present some of the main principles of human rights law that are relevant in the trade policy context, and how these impose legal obligations on states to carry out human rights impact assessments prior to adopting new trade agreements.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.003 | 0.007 |
| Scholarly communication | 0.007 | 0.007 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.004 | 0.004 |
| Insufficient payload (model declined to judge) | 0.021 | 0.002 |
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".