Bibliographic record
Abstract
Abstract How Coppola Became Cage chronicles Nicolas Cage’s early career and rise to fame, examining the formative performances that made him an icon of independent cinema in the 1980s and early 1990s. Drawing on more than 100 new interviews with Cage’s collaborators—including filmmakers David Lynch, John Patrick Shanley, Mike Figgis, Martha Coolidge, and Amy Heckerling—this book offers a revealing portrait of Cage’s origin story as a member of the Coppola family, his early roles in low-budget teen films, and his rise to stardom with memorable performances in cult films like Raising Arizona, Moonstruck, and Wild at Heart. The book examines how Cage drew on influences as eclectic as silent cinema and German Expressionism, while displaying an intense commitment to his performances both on- and off-screen. The book demystifies the actor’s on-screen eccentricities and argues that his commercial failures are as interesting as his successes. How Coppola Became Cage meticulously traces Cage’s career from 1981, when he was a young drama student at Beverly Hills High School, to 1995, when he gave an Oscar-winning performance as a suicidal alcoholic in Leaving Las Vegas.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.007 | 0.006 |
| Scholarly communication | 0.009 | 0.006 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.022 | 0.003 |
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".