Thornley, Davinia. Cinema, Cross-Cultural Collaboration, and Criticism: Filming on an Uneven Field. Basingstoke: Palgrave Macmillan, 2014.
Bibliographic record
Abstract
he issue of cinematic collaborations across cultures is discussed by Davinia hornley in her book through a close reading approach that encompasses the textual layers of ilms, but also the production levels in which the collaboration among cultures stands out as a source of conlict and learning.Her focus on indigenous cinema locates the analysis on examples from the Commonwealth countries of Canada, Australia, and Aotearoa New Zealand, exploring ictional ilms and documentaries produced in a collaborative process by both indigenous and nonindigenous crew and cast.Such partnership becomes a focal point in her discussion of how cinematic collaborations can help open up spaces of dialogue and self-expression for the indigenous groups that start in the production phase and must be carefully acknowledged in the criticism of such ilms.hornley sensibly calls attention to the singularity of indigenous ilms and how these works speak from very speciic places and cultures, extending her criticism and analysis not only to the role of nonindigenous members in indigenous ilms, but also exploring the indigenous participation in the artistic creations.Collaboration becomes a keyword for the production of cross-cultural ilms that ind themselves in the crossroads between the requirements of an industry inserted in a commercial context and the needs of recovering and transmitting the visual history of indigenous peoples.One of the main subjects discussed in her book is the aterlife of indigenous ilms and their contribution to society through a process of continuing conversation among the artistic creators and the audience, whether they come from an indigenous background or not.Although hornley seems to underline the positive outcomes of such interaction in terms of a greater awareness of indigenous culture and an efort to foster indigenous self-expression, she recognizes the shortcomings of crosscultural undertakings in the form of inequalities experienced in a power-sharing * Ketlyn Mara Rosa holds a degree in Letras Inglês (UFSC, 2006), an MA in English and Corresponding Literature (UFSC, 2015), and is currently a doctoral candidate at Programa de Pós-Graduação de Inglês at Universidade Federal de Santa Catarina.Her research looks into the issues of landscape and technology in representations of contemporary war in cinema, speciically in ilms that take place during the Afghanistan and Iraq wars.In recent years, Ketlyn has researched on portrayals of combat and violence in war ilms and miniseries.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.004 | 0.005 |
| Science and technology studies | 0.005 | 0.008 |
| Scholarly communication | 0.006 | 0.013 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.004 | 0.004 |
| Insufficient payload (model declined to judge) | 0.019 | 0.004 |
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".