Bibliographic record
Abstract
Au cours des dernières années, plusieurs pratiques culturelles et artistiques observées à Montréal remettent en question l’utilisation de la dichotomie classique d’espaces publics-privés. Pour pallier ce manque conceptuel, nous proposons, dans le cadre de cet article, de préciser la notion d’espaces équivoques. Par la présentation sommaire de cinq exemples de la région de Montréal, nous offrirons un aperçu des formes que peuvent prendre ces espaces. Puis, à partir d’une double étude de cas réalisée entre 2009 et 2011, nous démontrerons par quels processus le caractère équivoque de certains espaces émerge, s’accentue ou s’amenuise. Les résultats de cette recherche à caractère exploratoire permettront de fournir de nouveaux éclairages quant aux définitions d’espace public et privé et de soulever certaines pistes de réflexion quant aux relations qu’entretiennent les individus avec l’environnement urbain.
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 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.005 |
| Science and technology studies | 0.014 | 0.079 |
| Scholarly communication | 0.017 | 0.011 |
| Open science | 0.003 | 0.010 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.010 | 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; 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".