Conducting a human rights impact assessment of the Canada-Columbia free trade agreement : key issues
Bibliographic record
Abstract
The Parliamentary Standing Committee on International Trade (CIIT) has recommended that a Human Rights Impact assessment (HRIA) be carried out of the Canada-Colombia Free Trade Agreement (FTA). This paper explores some of the key issues that need to be considered in working towards the implementation of this recommendation. Section II explores what an HRIA is and what it can be expected to achieve. Section III sets out details of previous human rights impact assessments and social impact assessments of trade agreements and, on the basis of these experiences, makes a series of recommendations regarding how the Canada-Colombia FTA assessment should be conducted. Section IV considers the Recommendation of the CIIT for an HRIA, and suggests a model for oversight of the process. Section V provides outline methodologies for assessing the impacts of different types of FTA provisions – agricultural liberalisation provisions, investment provisions and labour protection provisions. Section VI summarises the key steps that should be demanded of any HRIA of the Canada-Colombia FTA and suggests some follow-up strategies. Finally, Appendix 1 provides references to key resources, which will be useful in further work on HRIAs of trade agreements. It is generally assumed throughout that the focus of the HRIA will be on the human rights impact of the FTA in Colombia rather than Canada, unless otherwise stated. This was the focus of the Standing Committee on International Trade and there do appear to be a far greater range of potential human rights impacts of the FTA in Colombia as opposed to Canada. The methodological approach would not change should the focus be instead on the potential for human rights violations in Canada.
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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 | 0.039 | 0.054 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.007 | 0.011 |
| Science and technology studies | 0.014 | 0.008 |
| Scholarly communication | 0.025 | 0.010 |
| Open science | 0.005 | 0.005 |
| Research integrity | 0.005 | 0.008 |
| 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".