Bibliographic record
Abstract
In recent years the international film industry’s attention has turned to highlighting the lack of gender equality in filmmaking. There has been much discussion on ways to increase production led by female filmmakers, and continuing pressure applied to various film festivals to exhibit their finished works. In Australia, this disunion has already been noticed and some Australian funding bodies are now compliant. So why are female led films the exception rather than the rule? Why are there only a handful of A-listed actresses that are entrusted to open films that wear the mantel of ‘women’s film or chick-flick”? In horror film circles the same problems arise: apart from the strong Final Girl archetype, female characters are often side-lined to be a girlfriend or victim that is at the mercy of the stronger male characters, or worse, an insane harridan whose film is categorised under the ‘psycho biddy’ subgenre. This article closely considers Canadian filmmaker Ursula Dabrowsky, a filmmaker based in Adelaide, Australia, in both the context of gender representation in her film Inner Demon (2014) and in terms of her status as a woman making genre cinema in the context of the contemporary Australian film industry.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 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 | 0.004 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.002 | 0.007 |
| Insufficient payload (model declined to judge) | 0.012 | 0.003 |
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".