Experimental Regulation to Address Modern Slavery and Forced Labour in Global Supply Chains: Canada's Passage of Transparency Modern Slavery Legislation
Bibliographic record
Abstract
This thesis explores Canada’s regulatory response to modern slavery in global supply chains. It investigates the factors which influenced Canada to enact transparency modern slavery legislation. It also analyzes Canada’s strategy of utilizing multiple soft and hard law governance and regulatory techniques to strengthen its response to modern slavery. Using a theoretical framework which combines global governance and regulation literature with literature regarding the national institutionalization of global norms, this thesis examines how international actors that comprise the global anti-slavery network disseminate anti-slavery and corporate accountability norms. These norms are subsequently filtered through a country’s domestic political economy, and are translated into either transparency or mandatory human rights due diligence (MHRDD) legislation. The qualitative methods used in this thesis were documentary analysis and key informant interviews. Key informant interviews in conjunction with an analysis of relevant reports and parliamentary debates provided insight into the influences behind Canada’s enactment of various governance and regulatory techniques. Doctrinal legal analysis, and an assessment of the various techniques implemented in Canada, revealed the effectiveness of the individual techniques and how they interacted with each other. This thesis found that Canada adopted a transparency law due to a combination of: (1) International norm diffusion via an epistemic, global anti-slavery network; and (2) Canada’s unique domestic political economy. Features of Canada’s domestic political economy, including its affiliation as part of the Anglosphere, and its powerful mining industry, ultimately determined the enactment of transparency legislation. The thesis also found that Canada’s use of multiple, increasingly hard law governance and regulatory techniques is currently ineffective as these techniques do not complement each other, and actually weaken Canada’s regulatory response to modern slavery. Consequently, labour standards have not improved for supply chain workers. This thesis posits that Canada should prioritize centering and empowering workers to protect their own rights.
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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 | 0.006 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.012 | 0.012 |
| Scholarly communication | 0.004 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".