Bibliographic record
Abstract
What do we, as feminists, need right now-from cinema, from archives, from our communities?How can filmmaking, film festivals, and social movements of the past inspire or befuddle us today?And what is at stake in selecting and presenting archival works by women to create new forms of community?Whether we hold space together in a movie theater or a virtual screening room, we cannot help but draw connections between the unfinished past and the open-ended present.Forging these links is the rallying cry of the feminist film curator.In this tenth-anniversary double issue of Feminist Media Histories, we argue that everyone can contribute to the collective project of archival film curating.Unrealized feminist histories pave the way to unforeseen social possibilities.In that spirit, please join our cabal of feminist archival film curators!We build on the labor, commitment, and imagination of many scholars before us.Writing forgotten women and other marginalized "makers" back into film history has been an energizing endeavor for feminist historiography, evinced in works by
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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 | 0.003 | 0.019 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.010 | 0.005 |
| Open science | 0.004 | 0.004 |
| Research integrity | 0.004 | 0.004 |
| Insufficient payload (model declined to judge) | 0.567 | 0.430 |
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".