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Record W2014175105 · doi:10.7202/015886ar

Comment faire apparaître Écho ? Soeurs, saintes et sibylles de Nan Goldin et Autoportrait en vert de Marie Ndiaye

2007· article· fr· W2014175105 on OpenAlexvenueno aff
Martine Delvaux, Jamie Herd

Bibliographic record

VenueProtée · 2007
Typearticle
Languagefr
FieldSocial Sciences
TopicAfrican history and culture studies
Canadian institutionsnot available
Fundersnot available
KeywordsHumanitiesArt

Abstract

fetched live from OpenAlex

Cet article étudie l’usage du portrait photo-textuel dans l’écriture autobiographique au féminin, par le biais de l’analyse parallèle de Soeurs, saintes et sibylles de Nan Goldin, et de Autoportrait en vert de Marie Ndiaye. Grâce au jeu entre photographie et littérature, ces objets intermédiatiques présentent une structure en écho (et reposent sur une série d’échos) qui permet de revoir le mythe d’Écho et de Narcisse ainsi que le rapport entre le visuel et le verbal. Goldin et Ndiaye fondent leur travail sur l’indécidabilité de la photographie pour écrire entre le mythe et l’histoire personnelle, le sacré et le profane. Par ailleurs, les oeuvres étudiées peuvent être lues comme des légendes et des échos dans le cadre du corpus de chacune des auteures. Ces pratiques intermédiaires mettent en place une série de résonances qui permettent aux femmes, à l’intérieur des deux textes, d’être vues et entendues. Par le vacillement entre portrait et autoportrait, ces pratiques réussissent à faire apparaître des visages là où il y avait des voix.

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)0.003
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.888
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.001
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0010.000

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 head0.018
GPT teacher head0.313
Teacher spread0.294 · 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

Citations2
Published2007
Admission routes1
Has abstractyes

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