Bibliographic record
Abstract
Depuis la fin du doctorat, j’ai eu la chance, à travers mon travail, de bénéficier de la bienveillance de nombreuses personnes: Gilles Godefroy, Alekos Kechris et Slawomir Solecki se sont chargés de la lourde tâche d’être nommés rapporteurs de ce mémoire. Je les en remercie sincèrement, de même que je remercie Damien Gaboriau, Alain Louveau, Jaroslav Neˇsetˇril, Stevo Todorcevic et Alain Valette de l’honneur qu’ils me font en acceptant de faire partie du jury. Je remercie aussi Jérôme Los, Laurent Regnier, et tous ceux qui ont fait en sorte que ce mémoire puisse être soutenu. Les résultats présentés ici ont été obtenus en différents coins du globe, mais trois d’entre eux méritent d’être cités à part: Calgary, Neuchâtel et Marseille. Rien de cela n’aurait été possible sans Claude Laflamme, Norbert Sauer, Alain Valette, et sans les membres de la clique marseillaise responsable de ma prise de poste au LATP. Pour cela, je tiens à leur adresser ma plus sincère gratitude. Ces mêmes résultats ont souvent été obtenus en (joyeuse) collaboration, et/ou
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 | 0.006 | 0.036 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.006 | 0.002 |
| Scholarly communication | 0.013 | 0.006 |
| Open science | 0.003 | 0.007 |
| Research integrity | 0.005 | 0.007 |
| Insufficient payload (model declined to judge) | 0.484 | 0.343 |
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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".