Bibliographic record
Abstract
Twenty years ago, noted film scholars Tom Gunning and Andre; Gaudreault introduced the phrase "cinema of attractions" to describe the essential qualities of films made in the medium's earliest days, those produced between 1895 and 1906. Now, The Cinema of Attractions Reloaded critically examines the term and its subsequent wide-ranging use in film studies. The collection opens with a history of the term, tracing the collaboration between Gaudreault and Gunning, the genesis of the term in their attempts to explain the spectacular effects of motion that lay at the heart of early cinema, and the pair's debts to Sergei Eisenstein and others. This reconstruction is followed by a look at applications of the term to more recent film productions, from the works of the Wachowski brothers to virtual reality and video games. With essays by an impressive collection of international film scholars - and featuring contributions by Gunning and Gaudreault as well - The Cinema of Attractions Reloaded will be necessary reading for all scholars of early film and its continuing influence
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.004 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.009 | 0.001 |
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".