Bibliographic record
Abstract
One of the most important Native films of all time, Atanarjuat, the Fast Runner tells a powerful and moving story about honor, betrayal, vengeance, and redemption. Set in the vast, visually stunning Arctic landscape, it was the first feature film written, directed, and acted entirely in Inuktitut, the language of Canada’s Inuit people. Canada’s top-grossing release of 2002, the film became an international phenomenon, receiving the prestigious Camera d’Or Award at the Cannes Film Festival and earning rave reviews from every quarter, including Margaret Atwood (“like Homer with a video camera”), Claude Lévi-Strauss, Jacques Chirac, and Roger Ebert. “The Fast Runner”: Filming the Legend of Atanarjuat takes readers behind the cameras, introducing them to the culture, history, traditions, and people that made this movie extraordinary. Michael Robert Evans explores how the epic film, perhaps the most significant text ever produced by indigenous filmmakers, artfully married the latest in video technology with the traditional storytelling of the Inuit. Tracing Atanarjuat from inception through production to reception, Evans shows how the filmmakers managed this complex intercultural “marriage”; how Igloolik Isuma Productions, the world’s premier indigenous film company, works; and how Inuit history and culture affected the film’s production, release, and worldwide response. His book is a unique, enlightening introduction and analysis of a film that serves as a model of autonomous media production for the more than 350 million indigenous people around the world.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.014 | 0.003 |
| Scholarly communication | 0.007 | 0.006 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.091 | 0.017 |
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".