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.
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 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) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.013 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.002 |
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".