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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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 teacher head, 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".