Decentering Otherness: Intercultural Encounters and Ethical Imperatives in Kuessipan
Bibliographic record
Abstract
An emerging First Nations cinema in Québec is progressively challenging both exotic or naïve representations of “the Indigenous Other” on the one hand, and falsely homogenous representations of a Québécois identity on the other. Kuessipan (2019), Myriam Verreault and Naomi Fontaine’s collaborative adaptation of Fontaine’s eponymous novel, is one of the most highly acclaimed productions within this shifting cinematic landscape. This article examines the ethical implications of the film’s narrative, form and production process as modes of intercultural encounter. It claims that by opening a space of productive exchange and solidarity—within the film, in its interpellation of viewers, and through Verreault and Fontaine’s engagements with each other and the public—Kuessipan is emblematic of the ethical ideals of “cinéma-monde” (Gott and Schilt).
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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 | 0.001 |
| Science and technology studies | 0.024 | 0.029 |
| Scholarly communication | 0.009 | 0.004 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.002 | 0.003 |
| 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".