Bibliographic record
Abstract
In 1949 the U.S. National Cancer Institute (NCI) and the Canadian Department of National Health and Welfare (DNHW) commissioned a film, eventually called Challenge. Science Against Cancer, as part of a major effort to recruit young scientists into cancer research. Both organizations feared that poor recruitment would stifle the development of the field at a time when funding for research was growing dramatically. The fear was that there would not be enough new young scientists to meet the demand, and that the shortfall would undermine cancer research and the hopes invested in it. Challenge aimed to persuade young scientists to think of cancer research as a career. This book is the story of that forgotten film and what it tells us about mid-twentieth century American and Canadian cancer research, educational filmmaking, and health education campaigns. It explores why Canadian and American health agencies turned to film to address the problem of scientist recruitment; how filmmakers turned such recruitment concerns into something they thought would work as a film; and how information officers at the NCI and DNHW sought to shape the impact of Challenge by embedding it in a broader educational and propaganda program. It is, in short, an account of the important, but hitherto undocumented, roles of filmmakers and information officers in the promotion of post-Second World War cancer research.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.009 | 0.004 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.817 | 0.719 |
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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".