Reading Nanook's Smile: Visual Sovereignty, Indigenous Revisions of Ethnography, and Atanarjuat (The Fast Runner)
Bibliographic record
Abstract
"Reading Nanook's Smile: Visual Sovereignty, Indigenous Revisions of Ethnography, and Atanarjuat (The Fast Runner)" puts ethnography and cinematic representations of Native Americans in crucial dialogue with the work of contemporary indigenous filmmakers. The author explores what it means for indigenous people "to laugh at the camera" as a tactic of what she calls "visual sovereignty," to confront the spectator with the often absurd assumptions that circulate around visual representations of Native Americans, while also flagging their involvement and, to some degree, complicity in these often disempowering structures of cinematic dominance and stereotype. She employs Atanarjuat (The Fast Runner) (2000), the first full-length feature film directed by an Inuit, Zacharias Kunuk, and produced by Igloolik Isuma Productions, Inc., a collaborative, majority Inuit production company, as her primary context for analysis to examine the ways this film is embedded within discourses about Arctic peoples that cannot be severed from the larger web of hegemonic discourses of ethnography. She does this first by discussing the pervasive images of Native Americans in ethnographic films and then by theorizing the ways that Atanarjuat intervenes into visual sovereignty as a film that successfully addresses a dual Inuit and non-Inuit audience for two different aims. More specifically, she interrogates how the Atanarjuat filmmakers strategically adjust and reframe the registers on which Inuit epistemes are considered with the twin, but not necessarily conflicting, aims of operating in the service of their home communities and forcing viewers to reconsider mass-mediated images of the Arctic.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.006 | 0.010 |
| Scholarly communication | 0.005 | 0.005 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.001 | 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".