Intersectionalities of Opportunism: Justin Trudeau and the Politics of “Diversity”
Bibliographic record
Abstract
For Prime Minister Trudeau, “equity talk” is central to his brand. He is a self-identified feminist, who embraces the terminology of equity, diversity, and inclusion, and borrows from discourses of intersectionality to frame his politics. There is now emerging literature that measures this “progressive” rhetoric against the reality, and this article seeks to contribute to that body of work. The focus of this article is especially on the use of “diversity” under the Liberal government of Justin Trudeau. I begin by outlining how “diversity” has always held a complicated place in feminist, critical race, post-colonial, and intersectional scholarship and activism. The concepts of diversity and difference are used to analyze socially-constructed inequalities based on gender, sex, race, ethnicity, class, age, sexuality, ability, citizenship, and geography ( CRIAW 2006 ; Dhamoon 2009 ), while also problematized for their superficial and instrumental applications. I argue that when held to scrutiny, Prime Minister Trudeau’s language on diversity falls into this latter categorization, where diversity is used as a descriptor rather than an analytical tool and as an opportunistic political device that undermines equitable public policy. This article focuses specifically on the equation of diversity with regional difference, in which provincial/territorial “diversity” is unquestioned, un-scrutinized, and naturalized. Provincial/territorial “diversity” is wholly celebrated. Using three policy examples (climate change, child care, and genetic discrimination), I argue that a substantive intersectional policy analysis reveals Trudeau’s celebration of regional policy “diversity,” as actually a defence of inequality and disparity.
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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 | 0.006 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.021 | 0.047 |
| Scholarly communication | 0.016 | 0.014 |
| Open science | 0.002 | 0.009 |
| Research integrity | 0.005 | 0.008 |
| Insufficient payload (model declined to judge) | 0.003 | 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".