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Record W3018769472 · doi:10.20429/aujh.2020.100107

“Un-American” Hollywood: Politics and Film in the Blacklist Era

2020· article· en· W3018769472 on OpenAlexaff
Natalie Jarosz

Bibliographic record

VenueArmstrong Undergraduate Journal of History · 2020
Typearticle
Languageen
FieldSocial Sciences
TopicEuropean history and politics
Canadian institutionsUniversity of Alberta
Fundersnot available
KeywordsHollywoodBlacklistingBlacklistPoliticsContext (archaeology)Movie theaterHistoryMedia studiesArt historySociologyPolitical scienceLaw

Abstract

fetched live from OpenAlex

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.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.715
Threshold uncertainty score0.397

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.029
GPT teacher head0.261
Teacher spread0.232 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designNot applicable
Domainnot available
GenreEmpirical

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".

Quick stats

Citations1
Published2020
Admission routes1
Has abstractyes

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