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Regional Scenes and Canadian Screens

2019· reference-entry· en· W2958897359 on OpenAlexaffabout
Darrell Varga

Bibliographic record

Venuenot available
Typereference-entry
Languageen
FieldSocial Sciences
TopicCultural Industries and Urban Development
Canadian institutionsNSCAD University
Fundersnot available
KeywordsHollywoodContext (archaeology)MillerCompetition (biology)Production (economics)Film industryMovie theaterIncentivePopular cultureMedia studiesAdvertisingSociologyHistoryArtVisual artsArt historyEconomicsBusinessMarket economyArchaeology

Abstract

fetched live from OpenAlex

This chapter situates the conditions of production, funding, and labor in Atlantic Canada along with the representations of culture and work (or the lack of work) on screen through an analysis of selected films notable either for their iconic status within the regional film scene or as vehicles through which the condition of labor and culture can be explored and illuminated. Film and television production in Atlantic Canada is a case study of what Toby Miller et al. have described as Global Hollywood (2008). Under this model, production is understood not by aesthetic design or cultural context but rather by the existence of subsidies and incentives developed in competition with other regions in Canada and throughout the world for the business of Hollywood. The films that are produced generally reflect the dominant ideological tendencies of Hollywood, though films may also express the potential for resistance—even if only partially articulated.

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)0.000
metaresearch head score (Gemma)0.001
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: Other · Consensus signal: Other
Teacher disagreement score0.062
Threshold uncertainty score0.453

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0020.005
Science and technology studies0.0130.004
Scholarly communication0.0080.001
Open science0.0010.002
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0360.001

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 head0.097
GPT teacher head0.295
Teacher spread0.199 · 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
GenreOther

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
Published2019
Admission routes2
Has abstractyes

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