Bibliographic record
Abstract
Rollerball , the Canadian-born director and producer Norman Jewison's 1975 vision of a future dominated by anonymous corporations and their executive elite, in which all individual effort and aggressive emotions are subsumed into a horrifically violent global sport, remains critically overlooked. What little has been written deals mainly with its place within the renaissance of Anglo-American science-fiction cinema in the 1970s, or focuses on the elaborately shot, still visceral to watch, game sequences, so realistic they briefly gave rise to speculation Rollerball may become an actual sport. Drawing on numerous sources, including little examined documents in the archive of the film's screenwriter William Harrison, this book examines the many dimensions of Rollerball 's making and reception: the way it simultaneously exhibits the aesthetics and narrative tropes of mainstream action and art-house cinema; the elaborate and painstaking process of world creation undertaken by Jewison and Harrison; and the cultural forces and debates that influenced them, including the increasing corporate power and growing violence in Western society in late 1960s and early 1970s. The book shows how a film that was derided by many critics for its violence works as a sophisticated and disturbing portrayal of a dystopian future that anticipates numerous contemporary concerns, including ‘fake news’ and declining literary and historical memory. The book includes an interview with Jewison on Rollerball 's influences, making, and reception.
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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 | 0.000 | 0.001 |
| 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 | 0.000 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.548 | 0.283 |
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".