Bibliographic record
Abstract
New essays on the life and work of veteran Hollywood filmmaker Delmer Daves From Destination Tokyo (1943) to The Battle of the Villa Fiorita (1965), Delmer Daves was responsible for a unique body of work, but few filmmakers have been as critically overlooked in existing scholarly literature. Often regarded as an embodiment of the self-effacing craftsmanship of classical and post-War Hollywood, films such as Broken Arrow (1950) and 3:10 to Yuma (1957) reveal a filmmaker concerned with style as much as sociocultural significance. As the first comprehensive study of Daves’s career, this collection of essays seeks to deepen our understanding of his work, and also to problematize existing conceptions of him as a competent, conventional and even naïve studio man. Key Features The first and only detailed study of this important American screenwriter, producer and director An international collection of original essays examining Daves’s films, including Broken Arrow , 3:10 to Yuma , Task Force and Spencer’s Mountain Contributors Fernando Gabriel Pagnoni Berns, Universidad de Buenos Aires (UBA) Matthew Carter, Manchester Metropolitan University Adrian Danks, RMIT University Andrew Howe, La Sierra University Józef Jaskulski, University of Warsaw Sue Matheson, University College of the North in Manitoba Andrew Patrick Nelson, Montana State University Fran Pheasant-Kelly, University of Wolverhampton Joseph Pomp, Harvard University John White, Anglia Ruskin University
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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 | 0.002 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.009 | 0.006 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.510 | 0.369 |
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".