Introduction. Festivals, Uncut: Queering Film Festival Studies, Curating LGBTQ Film Festivals
Bibliographic record
Abstract
‘Fearless, Shameless, Timeless.’ This book is born out of a paradox: while scholars have increasingly legitimated festivals as a semi-independent field of research within film and media studies, critics and arts organizers have long questioned the cultural relevancy of LGBTQ festivals. As early as 1982, Thomas Waugh wondered why (and whether) a new gay and lesbian film festival should be organized in Montreal. Similarly, B. Ruby Rich famously observed that queer festivals have simultaneously been ‘outlasting their mandate and invited to cease and desist’. In focusing on LGBTQ festivals’ conflicted temporalities and historiography, this book examines the disciplinary assumptions that structure festival studies: it questions the theoretical and political narratives implied in current festival scholarship. In particular, this book is concerned with festival studies’ quest for legitimacy: as a relatively recent field of academic research, festival studies has been burdened with justifying its object of research. Symptomatically, most books and dissertations on the topic start with a numbered description of the festival phenomenon. It is customary to highlight that thousands and thousands of festivals are organized each year. LGBTQ festivals are not an exception: for instance, Ger Zielinski asserts that queer festivals are ‘often second largest only to the IFF [International Film Festivals] in their respective city’. Similarly, Skadi Loist argues that ‘the LGBT/Q film festival scene has grown exponentially, covering most regions of the globe with about 230 active events on the circuit today’. The tendency to rely on statistics and to map out what has been coined as the festival circuit can difficultly be avoided: it justifies the relevancy of festival scholarship and is symptomatic of an academic climate in which scholars are constantly asked to evaluate the social impact of their research. It does, however, encode a set of assumptions about which festivals matter, take hard numbers as self-evident, and foreclose an examination of what constitutes a festival. Instead of participating in this collective effort to describe and justify the festival phenomenon, this book is concerned with analyzing the effects of festival studies’ theoretical and methodological frameworks – frameworks that tacitly structure our scholarship but are never fully acknowledged.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.004 | 0.002 |
| Scholarly communication | 0.006 | 0.005 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.047 | 0.012 |
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".