Lachapelle R, Bourque D (2020), Intervenir en développement des territoires . Presses de l’Université du Québec, « Initiatives », 148 pages
Bibliographic record
Abstract
Lectures Crague G (dir.) (2019) Faire la ville avec l'industrie.Métropoles et villes moyennes : 4 retours d'expériences.Presses des Ponts, Paris, 173 pages.Voy-Gillis A, Lluansi O (2020) Vers la renaissance industrielle.Éditions Marie B, Clichy, 91 pages.L'industrie française a connu de grandes difficultés liées à l'intensification de la mondialisation économique depuis les années 1980.Celles-ci ont culminé dans les années 2000 pour atteindre un pic dans la crise de 2008.Depuis la situation s'est améliorée.Les pouvoirs publics ont pris la mesure des enjeux en accompagnant les filières de la « Nouvelle France industrielle » dès 2013 ou les « Territoires d'industrie » depuis 2018.Pourtant, comme la crise sanitaire de la Covid-19 l'a révélé, beaucoup reste à faire pour développer et pérenniser une industrie en France, plus largement en Europe.
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 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.005 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.004 | 0.004 |
| Insufficient payload (model declined to judge) | 0.023 | 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".