Bibliographic record
Abstract
Une sémiotique computationnelle n’est pas une sémiotique de la computation ni des outils numériques ou de la relation personne-machine. Il s’agit d’une approche qui dans sa modélisation des artefacts sémiotiques construit des modèles computationnels qui en appellent à des modèles conceptuels et formels. Plus précisément, un modèle formel se construit en regard de ce qui a été conceptualisé à propos des artefacts sémiotiques à l’étude et est aussi contraint par les exigences de la modélisation computationnelle. Cet article traite des enjeux de la modélisation formelle pour une sémiotique computationnelle, à savoir : (a) la modélisation formelle dans les pratiques sémiotiques, (b) les définitions d’une modélisation formelle, (c) le rôle déterminant d’un modèle conceptuel sur la modélisation formelle, (d) la contrainte qu’exerce la modélisation computationnelle sur la modélisation formelle et, enfin, (e) les limites qu’impose la modélisation formelle à la recherche sémiotique.
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 | 0.011 | 0.022 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.003 | 0.019 |
| Scholarly communication | 0.010 | 0.016 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.003 | 0.006 |
| Insufficient payload (model declined to judge) | 0.010 | 0.002 |
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".