Bibliographic record
Abstract
On the Undecidability of Images in Communication.Fabrizio Scrivano [trans. lise hogan] We often attach a certain evidence to the image, while at the same time we attribute a function to such evidence that, in many cases, the image does not possess. What is extremely problematic is when the image is used in communication, when it happens that we find ourselves in a situation of stalemate before the image. Indeed, in order to be understood, the image in many cases requires aknowledge that it itself does not display, a competence that the image itself does not indicate. Perceiving the object-image and perceiving the meaning-image are often actions that do not coincide and that suggest a residue of information which can be associated with the permanency of a secret dimension of the image. This article seeks to focus on this theoretical question through the analysis of a few concrete examples. L'indécidabilité de l'image dans la communicationFabrizio Scrivano [traduit par lise hogan] On attribue souvent à l'image une évidence, tout en assignant à cette évidence une fonction que d'ailleurs elle ne possède pas. Ce fait est extrêmement problématique lorsque l'image est utilisée dans la communication, lorsqu'il arrive que devant l'image nous nous trouvons dans une situation d'impasse à l'essai. En effet, l'image, pour être comprise, dans de nombreux cas nécessite une connaissance qu'elle-même ne montre pas, une compétence que l'image ne suggère pas. Percevoir l'image-objet et percevoir le sens-image souvent ne sont pas des actions identiques qui présupposent un résidu d'information qui peut être associé à la permanence d'une dimension secrète de l'image. Cet article vise à se concentrer sur cette question théorique à travers l'analyse de quelques exemples concrets.
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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 | 0.010 | 0.037 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.006 | 0.033 |
| Scholarly communication | 0.007 | 0.020 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.006 | 0.014 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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.
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".