Bibliographic record
Abstract
Il arpente campagne, forets et bitume ; il hume, ecoute, observe. Reinstalle dans le village de son enfance, il frequente une poignee de paysans, s'occupe de Cyril, son filleul, s'eprend de Claire, bricole des meubles. Il parle peu, parle mal. La modernite et l'urbanisation de sa contree le rongent. Quant au chantier qui s'erige non loin, il en a peur. Mais ce molosse le subjugue aussi, le hante et l'emplit d'une etrange colere. Dans une langue traversee d'oralite, Parti voir les betes parle d'un amour sans bornes pour une terre condamnee a disparaitre. Ce roman fait entrer dans le regard de ceux qui eprouvent sans protection la beaute du monde. Anne-Sophie Subilia est nee en 1982. Belgo-suisse ayant vecu au Quebec, elle est l'auteure de Jours d'agrumes, un premier roman distingue par le prix ADELF-AMOPA 2014.
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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.006 | 0.005 |
| Scholarly communication | 0.007 | 0.005 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.052 | 0.022 |
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".