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Record W4416914701 · doi:10.7202/1121858ar

Remediating Toxic Images: Relating Practices for Representational and Environmental Justice

2025· article· en· W4416914701 on OpenAlexvenueno aff
Laurence Butet-Roch

Bibliographic record

VenueRACAR Revue d art canadienne · 2025
Typearticle
Languageen
FieldSocial Sciences
TopicParticipatory Visual Research Methods
Canadian institutionsnot available
Fundersnot available
KeywordsEconomic JusticeContext (archaeology)IntentionalityIdentity (music)

Abstract

fetched live from OpenAlex

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.

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 imitation

Not 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.

metaresearch head score (Codex)0.004
metaresearch head score (Gemma)0.027
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.861
Threshold uncertainty score0.981

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0040.027
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.347
GPT teacher head0.567
Teacher spread0.220 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

Study designNot applicable
Domainnot available
GenreEmpirical

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".

Quick stats

Citations0
Published2025
Admission routes1
Has abstractyes

Explore more

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