Crafting Canada’s Gender-Responsive Trade Policy
Bibliographic record
Abstract
Public concerns about the impacts of globalization and in particular the perception that benefits of trade have not been shared widely make it harder to continue to advocate for more and open trade at the multilateral level or in some cases at the bilateral level. The Government of Canada, therefore, has committed to making trade work for all, including women. Understanding the effects of trade on people in Canada is important. Global Affairs Canada (GAC) is undertaking a new analytical approach based on four complementary elements to help craft a coherent gender responsive and inclusive trade policy for Canada: (1) Research and analysis of the participation in trade of women-owned businesses in Canada; (2) Ex ante economic impact analysis using a computable general equilibrium (CGE) model and adding a labour market module; (3) Ex post quantitative assessment of free trade agreements (FTAs); and (4) Comprehensive dynamic gender-based analysis plus (GBA Plus) of a trade negotiation. The purpose of this chapter is to examine what Canada has been doing on these four elements and show how they are helping Canada craft a gender-responsive and inclusive trade policy so that others can determine whether this approach might be useful for application in their own countries.
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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 | 0.004 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.005 |
| Science and technology studies | 0.007 | 0.006 |
| Scholarly communication | 0.007 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.004 | 0.004 |
| Insufficient payload (model declined to judge) | 0.008 | 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".