Bibliographic record
Abstract
Cet article vise a definir, par induction analytique a partir de materiaux recueillis depuis plus de 4 ans dans des residences universitaires en France, aux Etats-Unis et au Canada, ce qu’est une plaisanterie d’inities. Les plaisanteries d’inities, objet principal de la presente etude, sont le resultat d’une filature d’interactions anterieures. Nous montrons ici que celles-ci sont toujours declenchees par une situation absurde desamorcee par le rire des inities in situ ou a posteriori. Elles sont alors activees et reactivees par les inities en presence d’un public. Nous definissons ainsi les inities comme les personnes presentes lors de la situation absurde et participant explicitement a la ou aux situations de desamorcage par le rire. Leur statut d’inities leur autorise trois actions : repeter la plaisanterie devant un public, exprimer explicitement la comprehension de la plaisanterie (si elle est dite par un autre initie) et negocier avec les autres inities l’inclusion d’un membre encore fluctuant. Cet article vise alors a distinguer les moqueries (rire excluant) des « taquineries » (rire integrateur) dans le cadre des plaisanteries d’inities. Les membres inities des groupes ne sont pas seulement des personnes qui connaissent l’origine de la reference passee, mais celles et ceux qui ont partage l’experience emotionnelle du desamorcage par le rire.
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 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.004 |
| Scholarly communication | 0.005 | 0.006 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.018 | 0.004 |
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".