Bibliographic record
Abstract
Timeline: The 1950s Murray Pomerance (Ryerson University, USA) Introduction: Movies and the 1950s Mary Beth Haralovich (University of Arizona, USA) 1950: Movies and Landscapes Kristen Hatch (University of California, USA) 1951: Movies and the New Faces of Masculinity Sumiko Higashi (State University of New York, USA) 1952: Movies and the Paradox of Female Stardom Rebecca Bell-Metereau (Texas State University, USA) 1953: Movies and Our Secret Lives Michael DeAngelis (DePaul University's School for New Learning) 1954: Movies and the Walls of Privacy Jon Lewis (Oregon State University, USA) 1955: Movies and Growing Up ... Absurd Barry Keith Grant (Brock University, USA) 1956: Movies and the Crack of Doom Murray Pomerance (Ryerson University, USA) 1957: Movies and the Search for Proportion Adrienne L. McLean (University of Texas, USA) 1958: Movies and Allegories of Ambivalence Arthur Knight (College of William and Mary, Williamsburg, USA) 1959: Movies and the Racial Divide Select Academy Awards, 1950-1959 Works Cited and Consulted Contributors Index
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 | 0.001 |
| 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 | 0.003 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.089 | 0.012 |
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".