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Record W2742180619 · doi:10.26522/vp.v11i2.1097

Imaginer, monter : la mémoire inachevée d’Auschwitz selon Georges Didi-Huberman

2014· article· fr· W2742180619 on OpenAlexaffvenue
Adina Balint-Babos

Bibliographic record

VenueVoix Plurielles · 2014
Typearticle
Languagefr
FieldComputer Science
TopicCultural Insights and Digital Impacts
Canadian institutionsUniversity of Winnipeg
Fundersnot available
KeywordsArtHumanitiesArt history

Abstract

fetched live from OpenAlex

Écorces de Georges Didi-Huberman est le « récit-photo » d’une déambulation à Auschwitz-Birkenau en juin 2010. Il y interroge quelques lambeaux du présent qu’il fallait photographier pour voir ce qui survit de la mémoire d’Auschwitz, ce qui pourrait mettre en œuvre le désir de ne pas en rester au deuil crispé du lieu. Cet article explore l’inachevé comme métaphore de l’écorce : d’une part, un morceau d’arbre, un « bout de texte », un fragment ; et d’autre part, une image, une surface pelliculaire qui ne couvre pas, mais se détache d’un corps. Qu’est-ce qu’on peut attendre d’une photo d’un morceau d’écorce de bouleau dans un endroit comme Birkenau ? J’analyse ainsi la question du montage entre texte et image, du partiel, de la production de « petites vérités » en tant qu’inachèvement d’un espace mémoriel. 
 
 To Imagine and to Mount: the Unfinished Memory of Auschwitz according to Georges Didi-Huberman
 
 ABSTRACT
 Écorces by Georges Didi-Huberman is the “récit-photo” of a wandering experience through the camps of Auschwitz-Birkenau in June 2010. The author takes photographs of some pieces of birch bark and reflects on what remains of Auschwitz today. Does this gesture have the power to shift him from mourning? In this article, I study the “inachevé” (the act of un-finishing) as a metaphor of the birch bark: on the one hand, this is a small part of a tree, a part of a text, a fragment; on the other hand, this is an image in our memory, a precarious surface that is detached from a body (of a tree). What to expect of a photographed piece of birch bark in such a place like Birkenau? I thus analyze the question of mounting (le montage) pieces of text and images, the idea of the partial and the production of “small truths” as figures of the unfinished space of our memory.

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)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Scholarly communication, Insufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.761
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0020.004
Open science0.0010.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.001

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 head0.059
GPT teacher head0.267
Teacher spread0.208 · 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
Published2014
Admission routes2
Has abstractyes

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