Bibliographic record
Abstract
Cet article explore le rôle de la littérature dans la posture psychiatrique. Les récits autofictionnels de Marie-Sissi Labrèche, Borderline, et de Michelle Lapierre-Dallaire, Y-avait-il des limites si oui je les ai franchies c’était par amour ok, montrent qu’une « attention à la langue, au délire, [et] à la création » (McDuff 2024 : 5) amène de nouvelles dimensions à une rencontre clinique. Dans cette magnifique « dilatation » et « densification » (McDuff 2024 : 7) que permet l’utilisation d’un langage littéraire, le ou la psychiatre se porte davantage à l’écoute de l’autre. Or, un travail de traduction entre les langages littéraire et biomédical devient nécessaire, et un dilemme par rapport à l’authenticité de son discours se pose. Existentiellement, nous sommes variablement tous impliqués dans cette quête de l’expression par le langage ; vouloir dire, dire bien, justement, pour réparer, honorer ou apaiser. Chercher et trouver ses mots, c’est un peu chercher et trouver l’autre.
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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.006 | 0.016 |
| Scholarly communication | 0.005 | 0.006 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.007 | 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".