“All Your Faves Are Problematic”: The Performative Spectatorship of Drunk Feminist Films
Bibliographic record
Abstract
Abstract This article looks at the work of the Drunk Feminist Film (DFF) collective from Toronto, Canada. DFF screenings offer interactive in-person and online events that combine watching popular Hollywood films with simultaneous live commentary, audience participation, and hashtag dialogs on Twitter. The article looks specifically at how the multiplatform hybridity of online and embodied participation allows feminist audiences to create collective spectatorial communities. DFF events emphasize paratextual conversation and reimagine how we can relate to movie narratives in the twenty-first century. By looking at a specific screening of The Craft (dir. Andrew Fleming, US, 1996), this article illustrates how DFF indexes the potential of engaging mainstream films while also engaging in feminist conversations about them. Problematic narrative aspects are discussed in real time by a community and are recorded via Twitter, resulting in digitally archived debates about how audiences collaboratively reinvent difficult but popular myths within their favorite films. As a result, the collective decenters mainstream films in favor of paratextual ephemera that negotiate the audience's pleasure in and critique of their favorite films.
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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 | 0.005 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.015 | 0.019 |
| Scholarly communication | 0.012 | 0.003 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 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".