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Record W2105534811 · doi:10.1017/cbo9780511606953.006

Millions like Us: National Cinema as Popular Cinema

2004· book-chapter· en· W2105534811 on OpenAlexaff
Jim Leach

Bibliographic record

VenueCambridge University Press eBooks · 2004
Typebook-chapter
Languageen
FieldEconomics, Econometrics and Finance
TopicCinema and Media Studies
Canadian institutionsBrock University
Fundersnot available
KeywordsMovie theaterNational cinemaArtArt historyHistory

Abstract

fetched live from OpenAlex

At the end of his essay on “The Concept of National Cinema,” Andrew Higson asks: “What is a national cinema if it doesn't have a national audience?” Throughout most of the world, Hollywood is popular cinema and, in countries with large film industries, the national cinema usually seeks to become popular by complex strategies that involve both differentiating its films from, and competing with, those of Hollywood. As to British cinema, box-office figures show that the national audience prefers Hollywood films, although the extent of this preference has often been exaggerated. As Thomas Elsaesser has insisted, “Hollywood can hardly be conceived, in the context of a ‘national’ cinema, as totally Other, since so much of any nation's film culture is implicitly ‘Hollywood.’” The cultural dynamics at work are described by Tom Ryall when he points out that “British film genres, although developed in the context of the national culture, were addressed to audiences steeped in the ‘foreign culture’ of Hollywood cinema.” Even when British films are not partially or completely funded by Hollywood studios, British filmmakers are aware that they must attract audiences whose expectations have been shaped by their experience of Hollywood films. In these circumstances, the drive for commercial success is often seen as a denial of the distinctive characteristics of the national culture. These considerations complicate the traditional approach to national cinema discussed in the Introduction. If we study a national cinema to discover what Siegfried Kracauer called “the psychological pattern of a people,” what does it mean if the people spend most of their time watching films from another national cinema? The problem is that “people” and “nation” are not synonymous.

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)0.002
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 score0.013
Threshold uncertainty score0.045

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0010.002
Scholarly communication0.0060.006
Open science0.0000.001
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0130.001

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 head0.034
GPT teacher head0.198
Teacher spread0.164 · 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
Published2004
Admission routes1
Has abstractyes

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