Bibliographic record
Abstract
Chapter 8 discusses the significant and global proliferation of werewolf films in the twenty-first century, and analyses their pervading and often transnational themes. It begins by tracing the dominant thematic preoccupations of 1990s werewolf movies into the 2000s, before coming to concentrate on a transnational cycle of she-wolf films that has arisen in the wake of fourth-wave feminism and the rise of the Me Too movement, including Denmark's <italic>When Animals Dream</italic> (2014), Canada's <italic>Female Werewolf</italic> (2015) and America's <italic>Wildling</italic> (2018). It then discusses a number of werewolf films concerned with the consequences of the Great Recession and resurgent fiscal and social conservatism in the Western world, including the American <italic>Late Phases</italic> (2014), the Canadian <italic>WolfCop</italic> (2014) and the British <italic>Howl</italic> (2015).
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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 teacher head, 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".