Identity politics and trade preferences: how the gendered and racialised effects of trade matter
Bibliographic record
Abstract
There is a considerable body of evidence that shows differentiated levels of support for trade based on identity characteristics, such as gender and race. Yet much of this work focuses on the US, with little evidence of how this relationship might operate amongst the US’ trade partners. This article examines how the racialised and gendered effects of trade matter for individuals’ trade preferences in Canada. Using an online, nationally representative survey, we combine a unique implementation of multidimensional preference scaling and two survey experiments to determine: (1) How people think of trade-offs between industry sectors that are affected by trade and (2) whether using identity priming about occupations as gendered or racialised affects their views about trade and state support for affected workers. Our observational data and pre-registered experiments demonstrate that Canadians’ trade attitudes reflect internalised beliefs about the gendered construction of the economy but are highly resistant to new information and either gendered or racialised identity priming, suggesting in-group favouritism and out-group anxiety are not activated in the same way outside of the US as within. This article thus contributes to growing work on the connections between gender inequality and racial discrimination in international trade politics.
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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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.005 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".