Bibliographic record
Abstract
The first collection of essays devoted to Hollywood director William Castle Often described as ‘the Master of Gimmicks’, William Castle is best known for the outrageous publicity stunts that characterised his genre films in the 1950s and ‘60s, including offers for an insurance policy against death by fright, vibrating seats, a skeleton that flew over the audience, and a ‘punishment poll’ to determine a film’s conclusion. But far from being ‘the world’s craziest filmmaker’, Castle was also a dependable studio director who made more than 50 films between 1944 and 1974, and who produced films for Orson Welles and Roman Polanski. ReFocus: The Films of William Castle assembles fourteen essays on the full sweep of Castle’s career, including his horror films, westerns, film noirs and more. With an influence felt on directors like Joe Dante, Robert Zemeckis and John Waters, this volume reappraises Castle’s legacy as an innovator as much as a showman. Contributors Hugh S. Manon (Clark University) Zachary Rearick (Georgia State University) Anthony Thomas McKenna (Shanghai Jiao Tong University) Murray Leeder (University of Calgary) Beth Kattelman (Ohio State University) Eliot Bessette (University of California, Berkeley) Alexandra Heller-Nicholas (University of Melbourne) Steffen Hantke (Sogang University) Michael Brodski (University of Mainz) Caroline Langhorst (University of Mainz) Michael Petitti (University of Southern California) Peter Marra (Wayne State University) Kate J. Russell (University of Toronto)
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.009 | 0.006 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.497 | 0.298 |
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".