Bibliographic record
Abstract
Resume Que serait Montreal sans la souverainete de ses arbres ? L’arbre est politique. Bertrand Laverdure sait parler aux arbres. Sans eux, les femmes et les hommes perdraient leur chemin et leur cœur. Une musique infinie, un vertige, un piano, ou une danse projette sa lumiere sur la ville. Que serait Montreal sans ce peuple vertical qui enseigne la douceur, l’espoir et l’humilite. Extrait du prologue « Ecrire aux arbres, c’est ecrire au temps, a la duree concrete, c’est echanger aussi avec le plus vieux reseaux de communication au monde. Les arbres et leurs « hyperracines » existent depuis plus de trois cents millions d’annees, le world wide web n’a plus ou moins que cinquante ans et n’est qu’une metaphore inspiree de leurs exploits d’adaptation. » Extrait LETTRE AU GRAND SAULE PLEUREUR DORE SUR LAFONTAINE COIN MORGAN Cher Skeletor, C’est l’hiver et tu es nu. Tu distribues tes os mous de doigts noueux autour de ton tronc de vieux printemps. Squelette marin, creature des profondeurs, tu fais claquer le froid sur ton instrument a fanons. Paisible comme une descente en apnee dans un gouffre bleu, tu assombris les circulaires. Tes baguettes couleur safran flattent mes reveries. Tu es la vigie d’un ruisseau mort. L'auteur Bertrand Laverdure vit a Montreal. Il a publie plus d’une quinzaine de livres et participe a plusieurs spectacles litteraires. Il a ete Poete de la Cite entre 2015 et 2017. Il a publie chez Memoire d'encrier Comment enseigner la mort a un robot?, (2015).
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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.006 | 0.003 |
| Scholarly communication | 0.008 | 0.003 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.035 | 0.006 |
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".