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Record W2035115953 · doi:10.3138/ijcs.49.229

Comparing the Politics of Urban Development in American and Canadian Cities: The Myth of the North-South Divide

2014· article· en· W2035115953 on OpenAlexvenueaboutno aff
Aaron Alexander Moore

Bibliographic record

VenueInternational Journal of Canadian Studies · 2014
Typearticle
Languageen
FieldSocial Sciences
TopicPolitical Systems and Governance
Canadian institutionsnot available
Fundersnot available
KeywordsPoliticsUrban politicsUrban planningPolitical scienceMythologyEmpirical researchEconomic growthLawHistoryEconomics

Abstract

fetched live from OpenAlex

The politics of urban development has been a major area of study in the United States for some time, and while the field is smaller in Canada, the study of urban development has always been an important aspect of the study of urban politics in this country. However, a fruitful discussion comparing Canadian and American cities has only emerged recently and is still largely in its infancy. Supposed institutional, legal, and cultural differences between the two countries continue to be cited as barriers to such research. This paper questions such assumptions. Drawing on existing empirical literature, and the author’s current and past research on the politics of urban development in Canadian cities, this paper argues that what cultural distinctions exist are minor and often peculiarities of specific cities, states, and/or provinces and that differences in planning law and institutions, though substantial, are not defined by a north–south divide. Rather, planning law and planning institutions vary significantly in both countries. Many Canadian jurisdictions have more in common with American jurisdictions than with fellow Canadian ones. These institutional differences do not act as a barrier to comparison, however, but are a useful means for gaining insight into the politics of urban development in both countries.

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)0.001
metaresearch head score (Gemma)0.001
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 score0.274
Threshold uncertainty score0.269

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
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 head0.036
GPT teacher head0.282
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

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 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

Citations2
Published2014
Admission routes2
Has abstractyes

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