Understanding Gender Expertise in the Post-Truth Era: Media Representations of Gender-Based Analysis Plus in Canada
Bibliographic record
Abstract
In this paper, we explore the post-truth era as a contextual factor in how gender expertise is constituted, challenged, and defended in policy discourse in Canadian context. Using post-structural policy analysis to explore the contours of media scrutiny and the resulting debate about gender-based analysis plus (GBA+), Canada’s approach to gender mainstreaming, we reveal that it was ultimately a debate about the role of gender/intersectional expertise within government. We demonstrate that GBA+, and the gender expertise informing it, was often represented in mainstream media as either a “political intervention” or as a “technical tool”, both of which reinforce traditional representations of policy expertise, including political neutrality and professional competence, which, in the past, have been used to justify the exclusion of “other” forms of knowledge. In unpacking these representations, we suggest that, even among critics of post-truth claims, post-truth discourse offers a new vocabulary, anchored in what Ringrose (2018, 653) refers to as “post-truth anti-feminism”, which emphasizes not simply identity politics, but also potential harm resulting from interventions based on feminist knowledge. We also suggest that such claims have resulted in a distancing between gender expertise and feminism, thus contributing to the erasure of feminist knowledge in policy contexts.
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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 | 0.010 | 0.023 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.006 | 0.009 |
| Science and technology studies | 0.031 | 0.033 |
| Scholarly communication | 0.025 | 0.007 |
| Open science | 0.002 | 0.009 |
| Research integrity | 0.004 | 0.006 |
| 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".