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Record W2921654638 · doi:10.1111/grow.12291

Diverse diversities—Open innovation in small towns and rural areas

2019· article· en· W2921654638 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueGrowth and Change · 2019
Typearticle
Languageen
FieldEconomics, Econometrics and Finance
TopicRegional Economics and Spatial Analysis
Canadian institutionsMcGill University
FundersSchweizerischer Nationalfonds zur Förderung der Wissenschaftlichen Forschung
KeywordsUrban agglomerationDiversity (politics)Economic geographyVariety (cybernetics)Work (physics)Rural areaBusinessGeographyEconomies of agglomerationRegional scienceEconomic growthEconomicsSociologyPolitical science

Abstract

fetched live from OpenAlex

Abstract It is generally accepted that cities and other forms of geographic agglomerations are conducive to innovation because their density and variety of firms, sectors and individuals create a diverse environment. However, a growing body of work shows that innovation also occurs in peripheral regions and small towns. Furthermore, work on rural social networks shows that diversity is multidimensional, and that along certain dimensions networks developed in rural areas are more diverse than those observed in cities. In this paper, we develop these arguments, then report our observations of seven successful firms in Swiss small towns. These firms benefit from at least three types of diversity: internal diversity; multiplexed interactions between workers at different hierarchical levels; and external diversity as firms reach beyond the region. We conclude that diversity conducive to firm‐level innovation is not a specifically urban attribute: at least some of its dimensions are present in small towns and more peripheral areas.

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.

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)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.199
Threshold uncertainty score0.996

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
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 head0.062
GPT teacher head0.204
Teacher spread0.142 · 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