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Record W2415158976 · doi:10.1177/1078087416636482

Conceptualizing Nonmarket Municipal Entrepreneurship: Everyday Municipal Innovation and the Roles of Metropolitan Context, Internal Resources, and Learning

2016· article· en· W2415158976 on OpenAlexaffabout
Richard Shearmur, Vincent Poirier

Bibliographic record

VenueUrban Affairs Review · 2016
Typearticle
Languageen
FieldSocial Sciences
TopicCultural Industries and Urban Development
Canadian institutionsMcGill University
Fundersnot available
KeywordsEntrepreneurshipMetropolitan areaContext (archaeology)AppropriationPoliticsNonmarket forcesCompetition (biology)Public serviceSociologyBusinessPublic relationsEconomicsPolitical scienceMarket economy

Abstract

fetched live from OpenAlex

Public-sector innovation and entrepreneurship usually refer to policies undertaken by public administrations or driven by urban regimes in view of furthering economic development. Some researchers study these processes from a management perspective; others critique them as vehicles of neoliberalization. However, scant attention has been paid to everyday technical and service innovation undertaken by municipal departments and employees. Although this innovation is usually not driven by markets, municipalities’ small size and geographic rootedness suggest it can be apprehended using concepts from firm-level studies. Our study of a municipal innovation competition in Quebec provides examples of everyday municipal innovation. We find that municipalities’ internal capacity determines their innovativeness, that learning occurs, and that the motivation and evaluation of everyday municipal innovation are not market-based. This calls into question the appropriation of the term urban entrepreneurship by urban political economists and invites students of cities to examine municipal entrepreneurial processes more closely.

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)0.002
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.829
Threshold uncertainty score0.468

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
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 head0.037
GPT teacher head0.289
Teacher spread0.253 · 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.

The models applied no category: nothing in the taxonomy fit this work.
Study designNot applicable
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

Citations39
Published2016
Admission routes2
Has abstractyes

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