MétaCan
Menu
Back to cohort
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 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.
aboutThe title or abstract carries a Canadian signal from the geographic lexicon.

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.

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)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.458
Threshold uncertainty score0.997

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.005
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 head0.081
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