Bibliographic record
Abstract
This is a review of “Un-American” Hollywood: Politics and Film in the Blacklist Era, a 2007 volume edited by Frank Krutnik, Steve Neale, Brian Neve, and Peter Stanfield. It argues that the American Left was involved with creating films of true significance in the Hollywood system, in the context of post-war House Un-American Activities Committee (HUAC) blacklisting. There is also an examination of the Popular Front between liberals and communists before post-war tensions drew them further apart. There is a chapter about the “new” and “old” waves of the left in the context of 1960s and 1970s American cinema. There is significant discussion of people other than screenwriters in the context of the HUAC blacklisting. The volume arguably leans towards social history – which is more appropriate for the argument of the volume than a methodology such as political history. There are fourteen chapters by different authors. The fourteenth chapter is an essential older text, “Red Hollywood.” “Red Hollywood” is followed by an afterword that its author wrote. It is recommended to have at least a mild-to-moderate knowledge of Hollywood history, and one may need to look up references while reading the volume. It is a very strong source that explores different nuances of history pertaining to the HUAC blacklisting, and 20th-century American left-wing politics.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.009 | 0.005 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.018 | 0.003 |
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".