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Unlikely Allies

2024· book-chapter· en· W4392079375 on OpenAlexaff
Charlie Keil, Denise McKenna

Bibliographic record

VenueOxford University Press eBooks · 2024
Typebook-chapter
Languageen
FieldEconomics, Econometrics and Finance
TopicCinema and Media Studies
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsHollywoodFilmmakingPublicityBoomCreativityFilm industryPolitical scienceMovie theaterAestheticsSociologyMedia studiesEngineeringVisual artsHistoryArtArt historyLaw

Abstract

fetched live from OpenAlex

Abstract As the filmmaking industry shifted operations to the West Coast, its efforts at self-definition coalesced in a singular cultural byword, “Hollywood,” nomenclature that came to stand for filmmaking as both industrial activity and cultural mythmaking. Crafting “Hollywood” out of Los Angeles was not merely a discursive exercise: during a crucial period of institutional development, the mid-1910s through the early 1920s, the industry needed to fix its place within the popular imagination. But how could filmmakers create an aura of businesslike stability and simultaneously promote flamboyant glamour and artistic creativity? By concentrating on three facets of this image—the role of managerial moviemakers, the formation of protective organizations to proactively fend off bad publicity, and the fusion of commercial growth with Southern California’s real-estate boom—the chapter examines the film industry’s varied efforts to manage and maintain its “institutional dualism” even as it defined the contours of “Hollywood.”

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 imitation

Not 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.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.003
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.161
Threshold uncertainty score0.538

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.003
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0050.004
Scholarly communication0.0050.005
Open science0.0010.003
Research integrity0.0020.004
Insufficient payload (model declined to judge)0.1610.037

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 head0.040
GPT teacher head0.188
Teacher spread0.148 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designQualitative
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

Citations0
Published2024
Admission routes1
Has abstractyes

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