Bibliographic record
Abstract
L’étude présentée s’efforce de composer raisonnablement un point de vue en ajustant, les unes aux autres, plusieurs exigences pressantes : (i) l’injection de l’intensité dans les structures élémentaires de la signification, injection qui invite à concevoir les traits sémantiques d’abord comme des vecteurs ; (ii) la prise de distance moins à l’égard de la narrativité stricto sensu qu’à l’égard de la narrativisation de la signification, ce qui est bien différent ; (iii) l’injection de l’intensité permet d’« armer » la notion de paradigme et de la doter du ressort qui lui manque encore ; (iv) le rapprochement entre la rhétorique et la sémiotique, lequel jusqu’à un certain point « va de soi », puisque la rhétorique vise, si l’on en croit les bons auteurs, à « donner de la vivacité, de la force ou de la grâce au discours » ; (v) enfin, pour ce qui est de l’adéquation, l’objet ici considéré, la noirceur, relève du non verbal, mais le plan du contenu s’avérant – jusqu’à preuve du contraire – commun, la différence entre les sémiotiques verbales et les sémiotiques non verbales n’est plus qu’une affaire de pondération, de balance entre les catégories contrôlant l’espace tensif.
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 | 0.003 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.006 | 0.009 |
| Scholarly communication | 0.010 | 0.007 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.005 |
| Insufficient payload (model declined to judge) | 0.015 | 0.003 |
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".