Bibliographic record
Abstract
L'insatisfaction humainePourquoi nos plaisirs sont-ils si fugaces?Souvent je me désole lorsque je termine un mets de choix dont je disposais en quantité limitée.Je regrette de ne pouvoir continuer à en manger.Par contre, si j'en dispose en grande quantité, je continue à en manger et je ressens simultanément le plaisir de continuer, la crainte d'arrê ter et la nausée qui s'annonce.En continuant à manger, le plaisir que m'apporte chaque bouchée diminue et l'annonce de la nausée s'accroît, pourtant je continue souvent parce que la crainte du manque s'accroît, elle aussi.Finalement bien sûr, tout a une fin et je quitte la table le ventre ballonné, alourdi par mes excès, vague ment nauséeux, et me reprochant une fois de plus le peu de maîtrise de mes comportements.Ce que je vis pour la nourriture de choix, la majorité des fumeurs l'éprou vent pour la cigarette et plusieurs personnes le ressen tent avec l'alcool.
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 | 0.005 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.004 | 0.032 |
| Scholarly communication | 0.008 | 0.018 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.006 | 0.008 |
| Insufficient payload (model declined to judge) | 0.016 | 0.003 |
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".