Intersectionalities of Opportunism: Justin Trudeau and the Politics of “Diversity”
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it