Conveying traditional Indigenous culture: From ethnographic film \nto community-based storytelling
Bibliographic record
Abstract
In the following chapters, I discuss several works of film, video and photography made since the early twentieth century depicting Inuit and other Indigenous peoples of North America. Their creators have been motivated by a desire to produce a record, through various methods of reconstruction, of past ways of life of their Indigenous subjects. In the context of these efforts, the question of how to structure the material to attract and hold the attention of an audience has been a primary concern. The films discussed in chapters two and three exemplify ethnographic filmmaking as a visual and narrative practice of salvage ethnography. In contrast, the films and videos discussed in chapters four and five are examples of Indigenous media—that is to say, media produced by Indigenous people and communities—that make use of ethnographic, or simply cultural, reconstruction in a way that assumes the continuing vitality of Indigenous cultures and a healthy balance between past and present. Focusing on the example of Canada’s first Inuit-made feature-length fiction film, Atanarjuat: The Fast Runner, I argue that the film’s success and significance is grounded in a respect for traditional Inuit storytelling practices and an experiential approach to teaching that uses video as a proxy for directly “showing how,” an effort to make traditional Inuit cultural memory and stories relevant to Inuit and wider audiences in the present and future.
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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 | 0.004 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.009 | 0.011 |
| Scholarly communication | 0.008 | 0.005 |
| Open science | 0.002 | 0.007 |
| 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".