‘Make it look a little like a festival’: Film exhibition and festival organizing at <i>Films Femmes Méditerranée</i> during the COVID-19 pandemic
Bibliographic record
Abstract
This article attends to the strategies of survival, affective labour, and practices of care – for women’s film work and publics – within women’s film festivals during the COVID-19 pandemic through the case study of Films Femmes Méditeranée, a women’s film festival based in Marseille, France. The festival was founded in 2007 to give visibility to the work of female filmmakers from the Mediterranean through a curated program of films, showcased in art-house theatres, with free screenings for young audiences, students, the unemployed, and people living on social benefits. Drawing on interviews conducted with workers at Films Femmes Méditeranée, I first provide an overview of the history and the mandate of Films Femmes Méditeranée in relation to the legacy of early women’s film festivals by bringing to light the work of care carried on in the context of Marseille. I will then analyse Films Femmes Méditeranée’s practices of curation and care through digital media during the pandemic. Through a consideration of the labour of film programming and festival organizing vis-à-vis social distancing measures and an examination of curatorial choices, I interrogate how Films Femmes Méditeranée has adapted to the pandemic crisis through digital media to maintain its commitment to fostering transnational approaches to women’s film culture and to provide a space for encounters between female filmmakers and audiences.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.020 | 0.009 |
| Scholarly communication | 0.006 | 0.003 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.005 | 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".