Remediating Toxic Images: Relating Practices for Representational and Environmental Justice
Bibliographic record
Abstract
Appuyé sur des recherches menées au sein et avec l’aide de la Première Nation Aamjiwnaang, cet article propose deux approches pour développer une pratique écophotographique critique et équitable : une sensibilité aux images qui hantent et l’emploi de méthodes participatives d’analyses du discours visuel, notamment les exercices d’élaborations visuelles. La première approche appelle à un engagement affectif avec les photographies, visant à identifier les représentations qui mettent pleinement en valeur le vivant – compris comme une existence complexe, dynamique et résonnante – plutôt que celles qui se contentent d’exposer une vie. La seconde approche invite les membres de la communauté à réfléchir et à intervenir directement sur des tirages photographiques. Déployées à Aamjiwnaang, ces méthodes ont entraîné des recommandations quant aux images à préconiser pour témoigner des dommages environnementaux causés par l’extractivisme sans pour autant risquer de (ré)inscrire les communautés et les écosystèmes touchés comme étant sacrifiables.
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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 | 0.016 | 0.026 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.009 | 0.022 |
| Scholarly communication | 0.013 | 0.012 |
| Open science | 0.002 | 0.012 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.015 | 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".