La politique des émotions suscitées par la formalisation esthétique de la photographie de Sebastião Salgado
Bibliographic record
Abstract
Sebastião Salgado est sans doute l’un des photographes les plus renommés du photoreportage contemporain. L’un des effets produits par la formalisation esthétique de sa photographie est l’émotion. Certains critiques qui s’opposent à l’approche discursive du photographe considèrent que cela empêche le spectateur de réfléchir sur la réalité représentée dans ses images. Le rôle des émotions dans le processus de réception de l’image est devenu une question très polémique (et peut-être la plus polémique) dans les débats qui jugent le caractère « bon » ou « mauvais » de la façon dont Salgado représente les personnes affectées par la souffrance sociale. Politic of emotions: The photography of Salgado Sebastião Salgado is one of the most famous photographers of the contemporary photojournalism. One of the effects produced by aesthetic formatting of his photography is the emotion. Some critics opposed to the discursive approach of the photographer consider that it prevents the Viewer to reflect on the reality represented in his images. The role of the emotions in the process of receiving the image has become a very polemical, and perhaps the most controversial, question laid on the table in the debates which consider the character « good » or « bad » in the Salgado’s way of representing people affected by social suffering.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.005 | 0.012 |
| Scholarly communication | 0.005 | 0.002 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.008 | 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".