Bibliographic record
Abstract
L’auteure souhaite faire la demonstration qu’au terme de sa production et de sa reception, l’image apparait naturellement relationnelle. Elle l’est d’un point de vue « techno-logique » (technique et logique, selon l’expression de Bernard Stiegler) ; sur le plan phenomenologique (par rapport a la perception dont elle temoigne et au devoilement de contenus qu’elle permet, selon Bergson et Heidegger) et quant a l’intermedialite susceptible de mettre a profit une ecriture chargee d’autres ecritures, une ecriture d’ecritures. L’image a eu un caractere relationnel aux moments ou Alberti a invente l’intersecteur au profit du dessin, Niepce, la camera obscura, pour la photographie, de meme que lorsque Muybridge s’est prete a une ecriture scientifique et esthetique de l’image en mouvement, pour le precinema. L’image peut etre assortie a une empreinte, dependre d’une inscription, ou etre produite par composition ou intermedialite, elle participe toujours d’un ouvragement et d’un devoilement. Ces derniers s’attachent tous deux aux formes et aux contenus generes par l’image et aux relations que ces contenus induisent avec le monde et ses ecritures. L’usager de l’image entre ainsi, jour apres jour, en relation avec des ecritures de l’image diversifiees et des ecritures de l’image soumises a des variations intermediales.
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 imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.006 | 0.025 |
| Scholarly communication | 0.015 | 0.013 |
| Open science | 0.001 | 0.007 |
| Research integrity | 0.003 | 0.008 |
| Insufficient payload (model declined to judge) | 0.025 | 0.004 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".