Bibliographic record
Abstract
L'auteur, beauceron quebecois, ancien paysan aux Etats-Unis, professeur de geographie a l'universite Laval, auteur d'une remarquee Geographie de nuit, est un inclassable. Un geopoete qui s'etonne de ce que nous suggerent les villes. L'echantillon qu'il donne des villes qu'il a durablement habitees a tout du patchwork. Ce qu'il aime par-dessus tout. Luc Bureau veut raconter ville et rendre compte de ville qui sa raconte. Cette deuxieme maniere de traiter ville n'est generalement pas pratiquee par les geographes qui delaissent les legendes, les recits, les anecdotes pour les donnees dont secheresse agglomeree devrait parler... On ne devoilera rien ici, sinon joyeuse liste dans laquelle vous pouvez cheminer en compagnie d'un guide comme vous m'en avez jamais eu: Quebec aux mollets tentants, Montreal sur ligne de partage des os, Paris entre Dieu et diable, Londres sorcellerie, brouillard et chaos, New York debout, Rome aux eaux douces et melodieuses, les nuits de Madrid, Florence ou la Venus couchee, Bruges est dans l'oeuf, le syndrome de Marseille, Le Caire lourd, Moscou a double visage, Berlin au-dela des murs, A moi... La Havane, Shanghai: une foret dense sans fin.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.016 | 0.004 |
| Scholarly communication | 0.008 | 0.004 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.166 | 0.029 |
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".