Bibliographic record
Abstract
On connait les beaux, mais aussi les betes. Une belle bete tout autant qu'un beau bete. Betement, les humains s'installent dans des situations ou l'echappee belle confine a la catastrophe. En fait, on s'en tire mal. Les 15 nouvelles de ce recueil portent toutes un nom associe aux animaux eleves en totems. Elles ecorchent les tabous. Elles explorent les relations fille-mere, homme-femme, amour-haine, conscient-inconscient. Elles ecorchent les lieux communs. Elles font fi du normal et de l'anormal. Un recueil de nouvelles remarquables, percutantes, intrigantes. Michel-Remi Lafond, dans Beaux et betes. Portraits en bestiaire, n'hesite pas a aborder des questions fort derangeantes qu'il traite avec humour et humeur. On ne peut que se reconnaitre ou identifier quelqu'un de notre entourage dans ces portraits que l'auteur dresse sans aucune complaisance et parfois d'une maniere crue. Michel-Remi Lafond a choisi Gatineau comme lieu ou se deroulent les actions et ou vivent les personnages. Il souhaite ainsi creer des espaces ou l'imaginaire colle au reel. Ce recueil de nouvelles puissant ne laissera personne indifferent !
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.002 | 0.004 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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; both teacher heads 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".