The universal and the environmental crisis: in search of new narrative modes
Bibliographic record
Abstract
La crise environnementale est un facteur qui accentue la crise de l’universel. Suivant l’idée de Souleymane Bachir Diagne selon laquelle pour universaliser, il faut décentrer le point de vue et diversifier les voix, cet article examine un échantillon de travaux de trois auteurs de différentes régions du monde qui abordent la crise écologique : Fifteen Million Years in Antarctica (2019) de Rebecca Priestley (Nouvelle-Zélande), A Bigger Picture: My Fight to Bring a New African Voice to the Climate Crisis (2021) de Vanessa Nakate (Ouganda) et deux photographies d’Edward Burtynsly (Canada). L’article vise à identifier les différentes stratégies narratives qui sont utilisées pour contribuer à construire une mémoire universelle, à témoigner d’une expérience révélatrice, ou à reconsidérer le point de vue en surplomb, stratégies qui participent toutes d’un changement de perspective décisif.
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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 | 0.011 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.015 | 0.067 |
| Scholarly communication | 0.013 | 0.023 |
| Open science | 0.002 | 0.013 |
| Research integrity | 0.003 | 0.006 |
| Insufficient payload (model declined to judge) | 0.007 | 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".