SMEs and human rights: bridging the accountability gap in the economy’s backbone. Navigating Canadian and global human-rights duties for local and supply-chain SMEs
Bibliographic record
Abstract
In Canada, small and medium-sized enterprises or SMEs are the backbone of Canada’s economy. SMEs’ contribution to human right accountability is still not investigated enough. These companies usually work under an “accountability gap," which means they don't have the awareness, knowledge and regulatory pressure to apply human rights due diligence ((HRDD), whereas large firms are subject to increased scrutiny under business and human rights (BHR) frameworks. This thesis looks at how corporate culture, policy, and enforcement affect Canadian SMEs' compliance with human rights obligations and why they stay behind in HRDD implementation. The study examines the gap between HRDD trends worldwide and SME reality in Canada, drawing on academic frameworks such as Political CSR, Institutional Theory, and Stakeholder Legitimacy. It highlights the main obstacles, a lack of state-led direction, supply-chain power disparities, and regulatory fragmentation, using a mixed-methods approach that includes case studies, interviews, and cross-sectoral analysis. The results show that SMEs react differently to human rights standards, depending on internal ethics or external constraints (such as domestic inspections or international sanctions). The study's policy propose, long-term reform would introduce tiered, risk-based HRDD duties and a single “one-front-door” support centre that integrates provincial employment-standards advice with federal supply-chain expertise.By bridging the accountability gap, Canada can align its SME sector with emerging global standards while safeguarding its economic resilience.
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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 | 0.007 | 0.014 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.015 | 0.013 |
| Scholarly communication | 0.008 | 0.004 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.002 | 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".