Evaluating Canada’s Inclusive Trade Agenda: A Care Ethics and Intersectionality Based Policy Analysis of the Canada-United States-Mexico Agreement
Bibliographic record
Abstract
The pursuit of international trade has been a cornerstone of global economic development and prosperity. However, current global trade systems often overlook the diverse needs and aspirations of women and marginalized communities, resulting in an uneven distribution of benefits and an exacerbation of socio-economic disparities. Economic growth through trade is central to Canada’s development, and inclusive trade agreements may be a useful tool for closing global gender gaps. In response to these challenges and building on insights gained from interviews with civil society representatives, this thesis sets forth a combined and enhanced intersectionality-based policy analysis (IBPA) and care ethics approach to inclusive trade. This enhanced approach incorporates considerations of care and a deeper intersectional approach to trade policy, where the role of care in the policy arena is often overlooked. The framework centers on adopting an enhanced IBPA-care ethics approach to understand the differentiated effects of trade on women by delving into the fundamental elements of care ethics and emphasizing the moral significance of caregiving. Thus, the approach places the well-being of individuals and communities at the forefront of policy considerations. Based on this theoretical framework, I conduct an in-depth examination of Canada’s inclusive trade policy, including an in-depth analysis of the Canada-United States-Mexico-Canada Agreement (CUSMA) and examples of other attempts by Canada to incorporate a gender-based analysis plus (GBA Plus) approach into its trade policy. Additionally, I conduct interviews with various civil society representatives to gauge Canadian government priorities, values, and interests in relation to Canada’s progress towards a more inclusive trade policy. Based on this analysis, I argue for the need for more robust gender chapters in all Canadian trade agreements that include enforcement mechanisms for monitoring and evaluation. Lastly, I suggest that policymakers adopt an enhanced intersectionality-based policy analysis and care ethics approach that places intersectionality and care at the center of inclusive trade policy to include marginalized stakeholders in the trade policy-making process.
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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 | 0.011 | 0.016 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.005 |
| Science and technology studies | 0.039 | 0.010 |
| Scholarly communication | 0.017 | 0.004 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.004 | 0.005 |
| 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".