Bibliographic record
Abstract
Partant du concept de « futurisme autochtone » (Dillon, 2012) et d’un corpus de trois adaptations animées de légendes (Petit Tonnerre [2009] de Nance Ackerman et Alan Syliboy, inspiré de légendes mi’kmaq ; le récit inuit Lumaajuuq de Alethea Arnaquq-Baril [2010] ; et La montagne de SGaana [2017] de Christopher Auchter, nourri de légendes et mythes haïda), je propose de réfléchir aux enjeux et bénéfices d’un tel format pour montrer que l’art autochtone se fait ici d'une part le tremplin de la survivance (Vizenor, 1994 ; 1998) et ouvre, d'autre part, la voie vers la résurgence (Simpson, 2013 ; 2016 ; 2017). Je partirai de l’hypothèse selon laquelle ces courts-métrages sous-tendent, grâce à l’animé et au travail de la bande son, une pensée décoloniale à travers leur investissement d’une esthétique futuriste : s’emparant du médium de l’animation, ils fonctionnent comme des lectures amplificatrices de la tradition véhiculée par les légendes et prennent leur place dans une large constellation suscitant un désir insatiable d'en savoir plus.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.004 | 0.019 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.011 | 0.001 |
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".