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A Smooth Ride? From Industrial to Creative Urbanism in <scp>O</scp>shawa, <scp>O</scp>ntario

2012· article· en· W1612024926 on OpenAlexaff
Elliot Siemiatycki

Bibliographic record

VenueInternational Journal of Urban and Regional Research · 2012
Typearticle
Languageen
FieldSocial Sciences
TopicCultural Industries and Urban Development
Canadian institutionsUniversity of British Columbia
Fundersnot available
KeywordsCreative classCreative CitiesMainstreamCreative cityLaggingUrbanismCreative industriesScholarshipCreativityMetropolitan areaRestructuringMistakeSociologyEconomic growthPolitical scienceEconomics

Abstract

fetched live from OpenAlex

Abstract In mainstream media, policy circles and academic scholarship, economic discourses have highlighted the importance of knowledge, creativity and innovation for generating economic growth. This has been translated into an urban planning and policy agenda which favours the establishment of research parks, innovation clusters, and especially universities along with amenities to attract creative‐class workers. In much of this literature universities are invested with an almost magical power to spur economic growth, and the benign language of ‘transition’ is used suggesting a rather seamless progression from one urban economic engine to another. Through analysis of policy documents and key informant interviews related to the establishment of a new university in O shawa, O ntario, this case study seeks to challenge the straightforward relationship that is assumed to exist between universities and local economic development. Like other lagging regions across the OECD attempting to repair their economies through creative and knowledge urbanism, O shawa's recent achievements are tempered by growing concerns about poverty, homelessness and inequality. Planners and policymakers that mistake the complexities of economic restructuring for a smooth ‘urban transition’ put their cities and citizens at risk of creating new problems out of efforts to improve local conditions.

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)0.003
metaresearch head score (Gemma)0.006
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: Not applicable
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.043
Threshold uncertainty score0.996

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.006
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0010.000
Research integrity0.0000.001
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 head0.188
GPT teacher head0.393
Teacher spread0.205 · 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

Citations19
Published2012
Admission routes1
Has abstractyes

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