Bibliographic record
Abstract
James Cameron (b. 1954) is lauded as one of the most successful and innovative filmmakers of the last thirty years. His films often break records, both in their massive budgets and in their box-office earnings. They include such hits as The Terminator, Aliens, The Abyss, Titanic, and Avatar. Part scientist, part dramatist, Cameron combines these two qualities into inventive and captivating films that often push the boundaries of special effects to accommodate his imagination. James Cameron: Interviews chronicles the writer-director's rise through the Hollywood system, highlighted by his can-do attitude and his insatiable drive to make the best film possible. As a young boy growing up in Canada, Cameron imagined himself an astronaut, a deep-sea explorer, a science fiction writer, or a filmmaker. Transplanted to southern California, he would go on to realize many of those boyhood fantasies. This collection of interviews provides glimpses of the filmmaker as he advances from Roger Corman's underling to king of the world. The interviews are drawn from a number of sources including TV appearances and conversations on blogs, which have never been published in print. Spanning more than twenty years, this collection constructs a concise and thorough examination of Cameron, a filmmaker who has almost single-handedly ushered Hollywood into the twenty-first century.
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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 | 0.006 | 0.017 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.017 | 0.006 |
| Scholarly communication | 0.005 | 0.005 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.003 | 0.006 |
| Insufficient payload (model declined to judge) | 0.041 | 0.010 |
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".