American cinema of the 1970s: themes and variations
Bibliographic record
Abstract
It was during the teens that filmmaking truly came into its own. Notably, the migration of studios to the West Coast established a connection between moviemaking and the exoticism of Hollywood. The essays in American Cinema of the 1910s explore the rapid developments of the decade that began with D. W. Griffith's unrivaled one-reelers. By mid-decade, multi-reel feature films were profoundly reshaping the industry and deluxe theaters were built to attract the broadest possible audience. Stars like Mary Pickford, Charlie Chaplin, and Douglas Fairbanks became vitally important, and companies began writing high-profile contracts to secure them. With the outbreak of World War I, the political, economic, and industrial groundwork was laid for American cinema's global dominance. By the end of the decade, filmmaking had become a true industry, complete with vertical integration, efficient specialization and standardization of practices, and self-regulatory agencies. Charlie Keil is an associate professor in the Department of History and the director of the Cinema Studies Institute at the University of Toronto. He is the author of Early American Cinema in Transition: Story, Style, and Filmmaking, 1907-1913. Ben Singer is an associate professor of film in the Department of Communication Arts at the University of Wisconsin, Madison. He is the author of Melodrama and Modernity: Early Sensational Cinema and Its Contexts.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.005 | 0.007 |
| Science and technology studies | 0.010 | 0.010 |
| Scholarly communication | 0.010 | 0.004 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 0.000 |
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".