Made in Montreal: a study of geography and priorities of small urban manufacturers in post-industrial Montreal
Bibliographic record
Abstract
À Montréal, il y a des dizaines de milliers de personnes travaillant dans le secteur de la fabrication. Cette recherche s'appuie sur des données désagrégées pour tracer les positions uniques des entreprises dans la communauté manufacturière de Montréal. Additionnellement, en utilisant les résultats de l'enquête de 95 répondants, le papier présente des explications pour lesquelles les petits fabricants urbains a choisi d'établir ou de rester dans les zones centrales de la ville. La méthodologie et les résultats de cette étude sont précédés par une revue de la littérature résumant la recherche académique concernant la distribution intra-‐métropolitaine des fabricants, la géographie et l'économie industrielle existante à Montréal, et les politiques et services en place concernant les fabricants urbains de Montréal. Encadrée ainsi, la question principale de recherche est divisée en deux parties qui aident à organiser ce document: Quels sont les types de fabrication qui ont survécu dans les zones centrales de Montréal et pourquoi ont-‐ils décidé d'implanter ou y rester? L'étude conclut en examinant les questions pertinentes de planification urbaine, y compris des recommendations de politiques et de services.
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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.006 |
| Science and technology studies | 0.005 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.010 | 0.000 |
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".