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Record W2766907090 · doi:10.1111/gec3.12349

Promoting innovation locally: Municipal regulation as barrier or boost?

2017· article· en· W2766907090 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.
fundA Canadian funder is recorded on the work.

Bibliographic record

VenueGeography Compass · 2017
Typearticle
Languageen
FieldEngineering
TopicTransportation and Mobility Innovations
Canadian institutionsUniversity of Toronto
FundersSocial Sciences and Humanities Research Council of Canada
KeywordsConvergence (economics)Key (lock)Political scienceEconomic systemEconomyBusinessEconomic growthEconomicsComputer science

Abstract

fetched live from OpenAlex

Abstract This paper examines a seemingly abrupt series of shifts that have the potential to completely transform cities as a direct result of a rising digital economy. It takes into account the convergence of research from three areas of study: (a) urban concentrations of innovative activities, (b) the platform economy, and (c) municipal regulation. A case study discussion about the role of municipal regulation in ride‐hailing, with particular emphasis on Uber, highlights debates regarding the challenges faced by municipalities amidst the prospect of renewed innovative activities and concomitant economic growth, alongside unknown risks. The paper concludes with a discussion about questions and approaches to understanding innovative activities in light of the emerging platform economy, and reviews key questions regarding the role that municipal regulatory decisions play in shaping the twenty‐first century city.

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 score0.455
Threshold uncertainty score0.581

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 head0.021
GPT teacher head0.267
Teacher spread0.246 · 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