The Feminist Museum Hack: Making a creative disruptive pedagogical, investigative and analytical tool
Bibliographic record
Abstract
How do we illuminate patriarchal ideologies that hide in plain sight in museums and art galleries and still play a powerful active role shaping andmobilising problematic gendered constructions that re-enforce gender and oppression? This question was central to our development of the Feminist Museum Hack, an innovative pedagogical, investigative, analytical and interventionist practice we have designed to use in the complex context of cultural institutions. In this article, we share the various components of the Hack, its aim to strengthen analytical and visual skills and connect disconnects of language and image. The Hack makes a valuable contribution to feminist adult education by enabling us to see the unseen of intrinsic patriarchal ideologies in museums. Further, the Hack works to develop imagination and sharpen an oppositional feminist gaze, giving us a sense of agency with which to disrupt the privileged authority of museums and unearth their problematic discursive, visual and rhetorical politics that have implications for seeing and knowing gender beyond their walls.
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 | 0.011 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.009 | 0.041 |
| Scholarly communication | 0.012 | 0.013 |
| Open science | 0.002 | 0.020 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.009 | 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".