Looking back to think forward: an exploration of what and how feminist exhibitions educate
Bibliographic record
Abstract
Curating temporary exhibitions with high visual appeal and compelling historical narratives is central to the work of museums. Yet what exhibitions teach as historical truth tends to concentrate on heteronormative masculine histories, reinforcing superiorities and whitewashing centuries of patriarchal oppression, control and violence. In response, museums in Canada and England are curating feminist exhibitions. I explored how 14 feminist exhibitions curated between 2016 and 2024 operated as feminist adult educators and specifically, what and how they educated. Findings how critical and creative strategies ‘herstory’ new subjects and agency, disrupt universalisms, make visible common differences, illuminate hidden ideologies, and out historical myths that continue to shape our gendered world. Feminist exhibitions not only minimise patriarchal power but interrupt negative perceptions of feminists through humour, satire and irony. By showcasing a range of issues, power dynamics and lives long denied visibility and intellectual credibility feminist exhibitions provide role models, create new knowledge and make a different world visible, hearable, feelable, and imaginable. By looking back they think forward, by cross fertilising the past with the present they offer not simply a new women’s history but a new history for all.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.010 | 0.019 |
| Scholarly communication | 0.009 | 0.008 |
| Open science | 0.002 | 0.008 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.008 | 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".