Bibliographic record
Abstract
In lieu of an abstract, here is a brief excerpt of the content: "Cher Francis, la meilleure façon de te rendre hommage aujourd’hui ce serait d’évoquer pour commencer tous ces bruits de couloirs qui agitent le pavillon Pasteur depuis quelques semaines.« Comment allons nous faire quand Francis sera parti à la retraite ? Qui va s’occuper des Lundis de la Philosophie ? » Toi encore, j’espère.Qui va, comme tu l’as fait toutes ces dernières années guider, conseiller, orienter les élèves dans le choix de leur sujet de thèse, la rédaction de leur projet et la détermination si importante de son encadrement ? Qui enfin les initiera à la philosophie ancienne ?Toutes ces questions pour dire que je ne vois pas très bien finalement pourquoi nous sommes réunis ici ce soir car il n’est tout simplement pas vraiment imaginable que tu partes ; et que nous allons déployer tous les trésors d’imagination possibles pour te retenir le plus possible, pour faire en sorte que tu restes encore dans les couloirs du pavillon Pasteur."
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 | 0.002 | 0.009 |
| 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 | 0.003 |
| Scholarly communication | 0.006 | 0.005 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.005 | 0.008 |
| Insufficient payload (model declined to judge) | 0.237 | 0.150 |
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".