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Towards Smarter Growth: Readiness and Deliberative Democracy in Mid-size to Rural areas

2020· article· en· W4408460158 on OpenAlexaffvenueabout
Victoria Agyepong

Bibliographic record

VenueRural Review Ontario Rural Planning Development and Policy · 2020
Typearticle
Languageen
FieldBusiness, Management and Accounting
TopicInnovation and Socioeconomic Development
Canadian institutionsUniversity of Guelph
Fundersnot available
KeywordsDeliberative democracyDemocracyPolitical scienceEconomic growthDevelopment economicsPsychologyEconomicsPoliticsLaw

Abstract

fetched live from OpenAlex

Smart growth has been considered as a financially and environmentally acceptable option to deal with the extensive growth in Canada since the late 1990s. Nonetheless, competing utilitarian, libertarian and egalitarian views on social justice, market forces, conflicting regulations, demographic shifts, and local conditions create gaps between theoretical acceptability and practical acceptability of Ontario Province’s smart growth policies at the municipal level. Furthermore, recent transitioning from traditional to technology-enabled smart growth have created conflicting views about the tenuity of the democratic role of Ontario’s municipal governments. Hence, the presentation will share what we know about the efficacy of deliberative democracy and readiness in the municipal planning process - instead of punitive measures, economic incentives and voting - to resolve the ‘smart growth gridlock’ in rural South-Western Ontario. Findings from this research will inform agri-food policy on best practices to improve acceptance of smart food system initiatives in rural Ontario.

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)0.004
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.812
Threshold uncertainty score0.378

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.004
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0060.009
Scholarly communication0.0040.002
Open science0.0010.003
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0040.000

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 head0.025
GPT teacher head0.264
Teacher spread0.240 · 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 designObservational
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

Citations0
Published2020
Admission routes3
Has abstractyes

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