Bibliographic record
Abstract
La plupart des pharmaciens s’accordent a dire qu’il faut eviter de reinventer la roue et qu’il y va de l’interet du patient et des professionnels de mettre en commun le fruit de notre travail! Personne ne peut etre contre la tarte aux pommes ou la vertu? Pourtant, la realite est souvent toute autre! Quand on propose de veritables echanges et qu’on s’apprete a poser un geste, on rencontre des resistances : « ils ne travaillent pas comme nous », « l’echange va-t-il etre equitable », « j’y ai mis tellement d’effort que j’ai peine a donner tout ca pour rien », « mieux vaudrait reviser davantage le contenu avant de le preter au risque d’avoir commis des erreurs et d’etre tenu responsable... de notre generosite ». En d’autres mots, vous est-il arrive de preter sans compter ou de recevoir sans critiquer des evaluations de medicaments preparees pour votre comite de pharmacologie, des protocoles de suivis systematiques, des tableaux de compatibilite, des guides de dilution, des logiciels-maison, des prises de position, votre plan strategique? Et l’altruisme a-t-il des limites?
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 | 0.010 | 0.030 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.007 | 0.010 |
| Scholarly communication | 0.013 | 0.020 |
| Open science | 0.002 | 0.008 |
| Research integrity | 0.007 | 0.012 |
| Insufficient payload (model declined to judge) | 0.068 | 0.025 |
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".