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Record W3121864020

Are Clusters Resilient? Evidence from Canadian Textile Industries

2017· preprint· en· W3121864020 on OpenAlexaffabout
Kristian Behrens, Brahim Boualam, Julien Martin

Bibliographic record

VenueRePEc: Research Papers in Economics · 2017
Typepreprint
Languageen
FieldEconomics, Econometrics and Finance
TopicRegional resilience and development
Canadian institutionsCenter for Interuniversity Research and Analysis on OrganizationsUniversité du Québec à Montréal
Fundersnot available
KeywordsResilience (materials science)GeocodingPsychological resilienceEconomic geographyClothingBusinessLine of businessTextileGeographyNatural resource economicsEconomicsBusiness modelCartographyMarketing
DOInot available

Abstract

fetched live from OpenAlex

We investigate whether plants inside and outside geographic clusters differ in their resilience to adverse economic shocks. To this end, we develop a bottom-up procedure to delimit clusters using Canadian geo-coded plant-level data. Focussing on the textile and clothing industries and exploiting the series of dramatic changes faced by that sector between 2001 and 2013, we find no evidence that plants in clusters are more resilient than plants outside clusters: they are neither less likely to die nor more likely to adapt by switching their main line of business. However, conditional on switching, plants in urbanized clusters are more likely to transition to services.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.002
metaresearch head score (Gemma)0.004
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.308
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.004
Meta-epidemiology (narrow)0.0000.001
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.000
Science and technology studies0.0010.001
Scholarly communication0.0010.000
Open science0.0020.001
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0000.000

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 head0.117
GPT teacher head0.322
Teacher spread0.205 · 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 teacher head, not a consensus.

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

Citations0
Published2017
Admission routes2
Has abstractyes

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