How aerospace clusters respond to the challenge of sustainability: a comparison of the Toulouse and Montreal clusters
Bibliographic record
Abstract
Abstract This paper examines how aerospace clusters help shape the innovation dynamics of aerospace manufacturers in the environmental transition to develop sustainable commercial aircraft. It intersects the economic geography, innovation, and sustainability literatures to develop a theoretical framework about the conditions that facilitate such a transition, and uses the case of two major aerospace clusters, Montreal and Toulouse, as a testing ground. Using a mixed-methods approach combining social network analysis and a series of interviews with some of the key actors in each cluster, the main findings of the study highlight a major difference between the two clusters: while in Toulouse the transition towards sustainability is a top-down approach orchestrated by the crucial role of public authorities, in Montreal the transition is a bottom-up one initiated by an active group of actors from aerospace firms and university research centers. The study also suggests some paradoxical outcomes of collaboration and competition between the two aerospace clusters during this process of environmental transition. Our study aims to contribute new insights to the literature on sustainability transitions in clusters and to develop implications for cluster research and policy-making.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.005 | 0.004 |
| Scholarly communication | 0.004 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.006 | 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".