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Record W2127570762 · doi:10.1111/0735-2166.00050

Slow Growth and Urban Development Policy

2000· article· en· W2127570762 on OpenAlex
Christopher Leo, WILSON B. BROWN

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

VenueJournal of Urban Affairs · 2000
Typearticle
Languageen
FieldEconomics, Econometrics and Finance
TopicHousing, Finance, and Neoliberalism
Canadian institutionsUniversity of Winnipeg
Fundersnot available
KeywordsGrowth managementArgument (complex analysis)ImmigrationEconomic growthPolicy developmentDevelopment economicsEconomicsPolitical scienceEconomic geographyPublic administration

Abstract

fetched live from OpenAlex

AbstractThis article distinguishes between cities experiencing high rates of growth and those growing more slowly and argues that 1) widely held North American assumptions to the contrary, slow growth is not a pathology; and 2) because we do tend to view it as a pathology, we fail to plan for it and instead follow policies more appropriate to rapidly growing centers. Using Winnipeg as the primary example of a slowly growing city, but drawing on a wide range of data, the article considers the following policy areas: housing, management of infrastructure, economic development, and immigration. In each of these areas the argument is that policies that may be defensible in rapidly growing centers are inappropriately followed in slowly growing cities where different lines of policy would be more beneficial. Appropriate policies for slowly growing cities are suggested and their merits evaluated.

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: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.737
Threshold uncertainty score0.667

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 head0.015
GPT teacher head0.200
Teacher spread0.185 · 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