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Record W4391751845 · doi:10.1080/26883597.2024.2313751

Understanding innovation in the context of local economic development: An analysis of cities’ innovation-based policies in Ontario, Canada

2024· article· en· W4391751845 on OpenAlexaffabout
Jesse Sutton, Selina Phan, Godwin Arku, John Hutchenreuther, Evan Cleave

Bibliographic record

VenueLocal Development & Society · 2024
Typearticle
Languageen
FieldSocial Sciences
TopicRegional Development and Policy
Canadian institutionsToronto Metropolitan UniversityWestern University
Fundersnot available
KeywordsRegional scienceContext (archaeology)Economic geographyLocal economic developmentBusinessEconomic growthGeographyEconomics

Abstract

fetched live from OpenAlex

The broad notion of innovation has permeated the consciousness of all levels of government globally. Literature suggests innovation is crucial for solving complex challenges and is essential for economies to maintain their competitiveness. However, there is limited understanding of what cities are doing to foster innovation. Also, it is unknown how local governments define innovation and measure the success of innovation-based policies. To address these gaps, this paper conducts a content analysis of cities’ economic development plans (n = 43) in Ontario, Canada. It finds that most cities, irrespective of size, implemented an array of innovation-based policies. Specific types of innovation-based policies were found to typically be grouped together in economic development plans, with three main policy clusters observed. Interestingly, the results indicate that most economic development plans fail to define innovation and do not employ meaningful metrics to measure the success of innovation-based policies.

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)0.002
metaresearch head score (Gemma)0.007
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.196
Threshold uncertainty score0.933

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.007
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0030.011
Science and technology studies0.0070.004
Scholarly communication0.0040.001
Open science0.0010.003
Research integrity0.0010.001
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 head0.081
GPT teacher head0.299
Teacher spread0.218 · 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 designQualitative
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

Citations2
Published2024
Admission routes2
Has abstractyes

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