MétaCan
Menu
Back to cohort
Record W7163558048 · doi:10.7202/1125351ar

Une exploration de la photographie vernaculairepar l’IA générative

2025· article· fr· W7163558048 on OpenAlexvenueno aff
Francois Gutherz

Bibliographic record

VenueSens public · 2025
Typearticle
Languagefr
FieldComputer Science
TopicCultural Insights and Digital Impacts
Canadian institutionsnot available
Fundersnot available
KeywordsAmateurPeriod (music)Work (physics)

Abstract

fetched live from OpenAlex

Cet article constitue le récit d’une exploration personnelle, menée en amateur et qui met à l’épreuve les outils d’intelligence artificielle générative appliqués à la photographie vernaculaire. Par des interpolations d’images à travers la représentation interne des modèles d’IA, cette démarche documentée met en lumière les artefacts et hallucinations caractéristiques de ces technologies. Loin de chercher à les corriger, elle en défend la puissance poétique et plaide pour une IA générative imparfaite, capable de dériver et de surprendre. (FG)

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)0.001
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 score0.010
Threshold uncertainty score0.033

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0010.003
Scholarly communication0.0040.002
Open science0.0000.002
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0100.001

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 head0.142
GPT teacher head0.339
Teacher spread0.197 · 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
Published2025
Admission routes1
Has abstractyes

Explore more

Same venueSens publicSame topicCultural Insights and Digital ImpactsFrench-language works237,207