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Record W2885383601 · doi:10.7202/1048837ar

Il ne suffit que de regarder : Éthique et analytique des images d’Auschwitz-Birkenau

2017· article· fr· W2885383601 on OpenAlexaboutno aff
William Delisle

Bibliographic record

VenueÉrudit (Université de Montréal) · 2017
Typearticle
Languagefr
FieldSocial Sciences
TopicDeath, Funerary Practices, and Mourning
Canadian institutionsnot available
Fundersnot available
KeywordsHumanitiesArtPhilosophy

Abstract

fetched live from OpenAlex

En raison de la tournée de Georges Didi-Huberman et de l’exposition Soulèvement, présentée par le Jeu de Paume, à travers le monde (Paris, Barcelone, Buenos Aires, México et Montréal), cet article fait un retour sur un ouvrage marquant du philosophe et historien de l’art français, Images malgré tout (2003, Éditions de Minuit). Dans un monde constamment submergé d’images, de représentations, comment pouvons-nous interpréter les images d’Auschwitz ? Sont-elles encore regardables ou demandent-elles trop d’engagement émotionnel ou intellectuel de celui ou celle qui les regarde ? Comment nous plaçons-nous dans l’imagerie de l’horreur ? Cet article propose un ajointement entre les pistes conceptuelles et éthiques de l’image que Georges Didi-Huberman propose dans son essai et les éléments élaborés par Sigmund Freud sur le désir et la pulsion scopique ainsi que ceux élaborés par Jacques Lacan sur le regard. À travers les exemples de Georges Didi-Huberman et l’apport de la psychanalyse, cet article laisse la place à une proposition éthique que l’on obtient grâce au compromis des multiples éléments qui composent l’image (ontologie, esthétisme, technique) et du regard, de son domaine subjectif et unique ; une éthique du malgré tout, du risque, basée sur les moyens de percer le réel, ce terrible réel des camps de concentration, de la machine-à-mort qu’était Auschwitz.

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 machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.002
metaresearch head score (Gemma)0.002
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.026
Threshold uncertainty score0.058

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0050.025
Scholarly communication0.0060.006
Open science0.0010.002
Research integrity0.0010.003
Insufficient payload (model declined to judge)0.0040.000

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 head0.017
GPT teacher head0.245
Teacher spread0.228 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
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
Published2017
Admission routes1
Has abstractyes

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