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Record W4378388067 · doi:10.1515/9781474483551

Race, Nation and Cultural Power in Film Adaptation

2023· book· en· W4378388067 on OpenAlexaboutno aff
Gillian Roberts

Bibliographic record

VenueEdinburgh University Press eBooks · 2023
Typebook
Languageen
FieldArts and Humanities
TopicShakespeare, Adaptation, and Literary Criticism
Canadian institutionsnot available
Fundersnot available
KeywordsRace (biology)Adaptation (eye)Power (physics)Gender studiesSociologyPsychologyPhysics

Abstract

fetched live from OpenAlex

Examines race and nation in postcolonial, settler-colonial, and Indigenous film adaptation Advances adaptation studies by offering a nuanced critique of the injunction against fidelity criticism 16 case studies of film adaptations across 7 chapters, detailing different modes of postcolonial, settler-colonial, and Indigenous film adaptation Wide-ranging comparative study, including literary and cinematic texts from Aotearoa/New Zealand, Australia, Canada, India, the UK, and the US In Race, Nation and Cultural Power in Film Adaptation , Roberts undertakes the first full-length study of postcolonial, settler-colonial and Indigenous film adaptation, encompassing literary and cinematic texts from Australian, Canadian, New Zealand, Indian, British, and US cultures. A necessary rethinking of adaptation in the context of race and nation, this book interrogates adaptation studies’ rejection of ‘fidelity criticism’ to consider the ethics and aesthetics of translating narratives from literature to cinema and across national borders for circulation in the global cultural marketplace. In this way, Roberts also traces the circulation of cultural power through these adaptations as they move into new contexts and find new audiences, often at a considerable geographical remove from the production of the source material. Further, this book assesses the impact of national and transnational industrial contexts of cultural production on the film adaptations themselves.

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)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Other · Consensus signal: Other
Teacher disagreement score0.760
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
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 head0.047
GPT teacher head0.215
Teacher spread0.168 · 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.

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

Citations1
Published2023
Admission routes1
Has abstractyes

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