The Tortoise and the Hare: Industry Clockspeed and Resilience of Production and Knowledge Networks in Montréal’s Aerospace Industry
Bibliographic record
Abstract
Abstract A central challenge in current cluster policy discussions is how to build innovative clusters that are resilient to external shocks. We examine the Montréal aerospace industry to explore cluster resilience. The case is interesting since it recently experienced two industrial shocks: Boeing 737 MAX crashes in 2018 and 2019 and Bombardier’s sell-off of its flagship CSeries in 2020. Surprisingly, in the wake of the two radical disruptions, the cluster fared quite well in terms of employment and export performance. Using the method of abductive reasoning to find a-matter-of-course explanation of the surprising case, we observe that a low speed of aircraft development and production – a low industry clockspeed – stabilizes local production and knowledge networks through five mechanisms: long-term contracting, R&D cost sharing, production planning, social networking, and technology solidifying. Inspired from the case, we theoretically explore how fast (e. g., fashion and cellphones or the hare) and low (e. g., shipbuilding and aerospace or the tortoise) industry clockspeeds lead to different configurations of firm relations and are thus associated with different types of economic resilience.
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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 | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 0.004 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.007 | 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".