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Record W1561004052 · doi:10.1111/ecge.12009

Competitiveness by Design: An Institutionalist Perspective on the Resurgence of a “Mature” Industry in a High‐Wage Economy

2013· article· en· W1561004052 on OpenAlexafffund
Carolyn J. Hatch

Bibliographic record

VenueEconomic Geography · 2013
Typearticle
Languageen
FieldSocial Sciences
TopicCultural Industries and Urban Development
Canadian institutionsUniversity of Toronto
FundersSocial Sciences and Humanities Research Council of CanadaUniversity of Toronto
KeywordsContext (archaeology)Agency (philosophy)Competitive advantageWageBusinessEconomicsEconomic geographyIndustrial organizationEconomyEconomic systemMarket economyMarketingSociology

Abstract

fetched live from OpenAlex

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.

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 imitation

Not 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.

metaresearch head score (Codex)0.008
metaresearch head score (Gemma)0.004
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.012
Threshold uncertainty score0.057

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0080.004
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0030.001
Science and technology studies0.0060.060
Scholarly communication0.0120.006
Open science0.0020.005
Research integrity0.0020.002
Insufficient payload (model declined to judge)0.0020.000

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.

Opus teacher head0.029
GPT teacher head0.266
Teacher spread0.236 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations31
Published2013
Admission routes2
Has abstractyes

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