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Record W2068209703 · doi:10.7202/1001068ar

Images technologiques : ce qu’il advient de la mémoire

2011· article· fr· W2068209703 on OpenAlexaffvenue
Yves Bédard

Bibliographic record

VenueCinémas Revue d études cinématographiques · 2011
Typearticle
Languagefr
FieldComputer Science
TopicCultural Insights and Digital Impacts
Canadian institutionsUniversité de MontréalUniversité du Québec à Montréal
Fundersnot available
KeywordsHumanitiesArt

Abstract

fetched live from OpenAlex

Avec l’apparition de la photographie, l’image a cessé d’être uniquement représentative, elle est devenue aussi mémoire du monde. Et le cinéma, en l’animant et en se repliant sur le réel comme son double, a fait d’elle un souvenir « vivant ». Mais il a tôt fait cependant de jouer de ses apparences et de construire des images dont le semblant de réel correspond à une mémoire non plus « objective » mais fictive, et où le monde apparaît moins comme un point de départ que comme un modèle à copier. Depuis, les développements technologiques de l’image ont accentué cette coupure : avec les images de synthèse et la télévision interactive, le spécialiste comme le spectateur moyen élaborent le spectacle personnalisé des événements qu’ils ont sous les yeux. L’écran est devenu la scène d’une mémoire du virtuel.

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)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Scholarly communication
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.875
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.002
Science and technology studies0.0000.002
Scholarly communication0.0020.004
Open science0.0020.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0000.000

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 head0.145
GPT teacher head0.294
Teacher spread0.149 · 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 designTheoretical or conceptual
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

Citations4
Published2011
Admission routes2
Has abstractyes

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