The Future of Trade is Inclusive: Canada’s Approach to Globalised Free Trade
Bibliographic record
Abstract
In response to people feeling left behind from experiencing trade-related gains, Canada has developed “inclusive” trade policies that attempt to redistribute trade- related opportunities to traditionally underrepresented groups like women, indigenous peoples, and small to medium sized enterprises (SMEs). While Canada has framed “inclusive” policies as being both socially and economically beneficial, the policies have been met with apprehension due to the impression that “inclusive” policies promote non-trade values. Despite the schism of “trade values” and “non-trade values”, the types of measures included in free trade agreements have expanded and adapted to societal and economic needs over time. Given the discontent surrounding globalized free trade, “inclusive” policies offer a path forward that could be both socially and economically more sustainable. This thesis endeavours to investigate if and how Canada’s inclusive trade policies have been adopted in the text of agreements, what impact the measures might have for expanding the categories of persons who benefit from globalized free trade, and whether inclusive policies create a tangible impact for the three target categories analysed: gender, Indigenous peoples and small to medium sized enterprises (SMEs). Specifically, this thesis examines inclusive measures within five recent trade agreements: the Comprehensive and Progressive Trans-Pacific Partnership (CPTPP), Canada-European Comprehensive Economic and Trade Agreement (CETA), Canada-United States-Mexico Agreement (CUSMA), Canada-Chile Free Trade Agreement and Canada-Israel Free Trade Agreement. The overarching goal of this thesis is to offer a practical solution that may ameliorate the discontent surrounding globalised free trade in a socioeconomically sustainable method based on Canada’s approach to inclusive trade.
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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 | 0.005 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.006 |
| Science and technology studies | 0.035 | 0.029 |
| Scholarly communication | 0.024 | 0.007 |
| Open science | 0.002 | 0.009 |
| Research integrity | 0.005 | 0.009 |
| Insufficient payload (model declined to judge) | 0.005 | 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".