Bibliographic record
Abstract
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.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.009 | 0.023 |
| Scholarly communication | 0.006 | 0.005 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".