Competitiveness by Design: An Institutionalist Perspective on the Resurgence of a “Mature” Industry in a High‐Wage Economy
Bibliographic record
Abstract
Abstract In the midst of the widespread, long‐term economic downturn throughout theCanadian manufacturing landscape, the contract (or office) furniture sector has demonstrated resilience and vibrancy. The study reported here investigated the institutional foundations of innovation and competitive advantage in this dynamic, design‐led, export‐oriented manufacturing sector. It connects to ongoing work in economic geography and the social sciences to enhance economic geographers' understanding of the role of institutions in shaping the practices of firms and competitive outcomes and seeks to advance a more agency‐centered institutionalist economic geography. The study focused on three dimensions of industrial practices: (1) the use of training and investments in technology, (2) the nature of employment relations, and (3) the use of design. The analysis reveals that the most globally competitive firms operating in aCanadian institutional context prosper by learning a set of production practices and the value of design‐intensive products from the embodied knowledge of their founders, who have lived, studied and worked in high‐wage, coordinated market economies of continentalEurope. The ability of these entrepreneurs to transfer industrial knowledge from continentalEurope toCanada has had direct benefits for learning and innovation processes that are critical to the synthetic knowledge base of this sector. The empirical analysis entails a sector wide survey questionnaire (N = 220) as well as 55 in‐depth interviews with senior managers, production workers, and designers from a subset of leading firms.
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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 | 0.008 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.006 | 0.060 |
| Scholarly communication | 0.012 | 0.006 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".