The Implementation of Economic, Social and Cultural Rights in Canada: Between Utopia and Reality
Bibliographic record
Abstract
Canada has been at the forefront of the recognition of human rights, including economic, social and cultural rights (ESC rights) in the international scene. As a party to the International Covenant on Economic, Social and Cultural Rights,1 Canada has, over the years, implemented in legislation and case-law some ESC rights such as the right to health, education and social welfare.While ESC rights were not explicitly identified in the Charter of Rights and Freedoms,2 which forms part of the Canadian Constitution, ESC rights in different forms have received some protection in the Canadian legal order. An analysis of the Canadian record with respect to ESC rights demonstrates the immense gap between a glorified image of Canada as an international human rights proponent (the ‘utopia’) and the actual implementation of internationally recognized human rights in Canada (the ‘reality’). As Canada is bound to face major transformational changes to its economy and social fabric in the years to come, the Courts will have to adapt quickly and efficiently to ensure a smooth transition. This paper overviews the evolution of the case-law on ESC rights in Canada in light of its international obligations, and suggests, the relevant ESC rights jurisprudence signals a disconnect with Canada’s international obligation ‘requiring progressive implementation to the maximum of available resources by all appropriate means.’
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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 | 0.008 | 0.018 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.006 |
| Science and technology studies | 0.024 | 0.037 |
| Scholarly communication | 0.017 | 0.003 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.004 | 0.007 |
| Insufficient payload (model declined to judge) | 0.003 | 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".