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Record W3121935986

The Rise of Hollywood East: Regional Film Offices as Intermediaries in Film and Television Production Clusters

2013· article· en· W3121935986 on OpenAlexaboutno aff
Pacey Foster, Stephan Manning, David Terkla

Bibliographic record

VenueSussex Research Online (University of Sussex) · 2013
Typearticle
Languageen
FieldSocial Sciences
TopicCultural Industries and Urban Development
Canadian institutionsnot available
Fundersnot available
KeywordsHollywoodIntermediaryProduction (economics)Cluster (spacecraft)BusinessCreative industriesFilm industryBox officeService (business)AdvertisingEconomic geographyMarketingPolitical scienceMovie theaterGeographyEconomicsVisual artsComputer science
DOInot available

Abstract

fetched live from OpenAlex

Prior research on project-based organizing in creative industries has emphasized the importance of regionally embedded institutions, creative networks and intermediaries in the development of regional project ecologies. Recently, film and television production in the United States has expanded beyond traditional clusters in Hollywood and New York to new locations in the United States, Canada and overseas, raising important questions about the dynamics of increasingly mobile creative project networks. Using data on the Massachusetts film and television industry between 1998 and 2010, it is argued that regional film offices play an increasingly important role as network intermediaries in connecting mobile creative professionals and project entrepreneurs from outside a cluster with labour pools, service providers and production locations inside a cluster on a project-by-project basis.

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)0.003
metaresearch head score (Gemma)0.006
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.018
Threshold uncertainty score0.035

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.006
Meta-epidemiology (narrow)0.0000.001
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0030.003
Science and technology studies0.0050.006
Scholarly communication0.0100.009
Open science0.0010.009
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0090.001

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 head0.072
GPT teacher head0.331
Teacher spread0.259 · 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 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

Citations0
Published2013
Admission routes1
Has abstractyes

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