Les industries aérospatiales en Amérique du Nord : entre permanences et recompositions territoriales
Bibliographic record
Abstract
Au courant des annees 1990, l'industrie aerospatiale (aeronautique + spatial) americaine a vecu une phase de restructuration afin de s'ajuster aux nouvelles conditions du marche. Les constructeurs ont du faire face a une baisse sans precedent des budgets militaires et la forte competition internationale dans le domaine des productions civiles a exacerbe le jeu des transactions entre entreprises. L'objet de cette recherche etait double : d'abord comprendre la logique des localisations de ce secteur, et saisir ensuite comment tous ces elements avaient affecte les espaces de production. Le traitement effectue a partir des County Business Patterns, pour les Etats-Unis, et des recensements de Statistiques Canada, ont permis de proceder a un releve exhaustif des sites a partir de donnees d'emploi. L'analyse de leur dynamique porte sur une periode de 20 ans. Les principaux resultats montrent la permanence des localisations initiales : malgre les phases d'expansion et les crises, le dispositif d'origine reste en place ; il temoigne des limites de diffusion de certains savoir-faire. Ces dernieres annees neanmoins, le glissement du centre de gravite de l'industrie se fait en direction des lieux specialises dans la production civile. La resistance des espaces de la production militaire est variable a en juger la progression differenciee des activites de haute technologie pour chacun.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.005 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".