Bibliographic record
Abstract
In response to calls by feminist cultural theorists to develop means to unmask patriarchy, the system of power that lies at the heart of museums that maintain problematic hierarchical binaries of masculinity and femininity, we designed the Feminist Museum Hack. The Hack draws on theories of representation, feminist critical discourse analysis and visual methodologies/literacy to operate as a critical and creative practice that can be adapted to any museum context. The primary aim of the Hack – a methodology and pedagogy – is to provide a lens through which adults can see the unseen of patriarchy and how it hides so cleverly in plain sight in the museum’s practices of representation. In this article, we use examples of how we have used the Hack as researchers and educators in various museum settings to expose, decode and disrupt the hegemonic gendered messages in the images, displays, curatorial statements, labels and even in object placement and stagecrafting. We also show how the Hack functions as a practice of ‘direct agency’, a means to re-write and engage with museum narratives. We argue that the Hack is an important and innovative practice because it turns museums into spaces of ‘pedagogic possibility’ – sites where we can learn new strategies of feminist opposition to counter the male gaze and its ability to define women’s lives.
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.
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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.011 | 0.008 |
| Scholarly communication | 0.006 | 0.007 |
| Open science | 0.001 | 0.009 |
| Research integrity | 0.002 | 0.006 |
| Insufficient payload (model declined to judge) | 0.080 | 0.015 |
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".