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Record W2120023313 · doi:10.1068/c1g

The Planning — Policy Connection in US and Canadian Economic Development

2006· article· en· W2120023313 on OpenAlexaboutno aff
Laura A. Reese

Bibliographic record

VenueEnvironment and Planning C Government and Policy · 2006
Typearticle
Languageen
FieldSocial Sciences
TopicUrban Planning and Governance
Canadian institutionsnot available
Fundersnot available
KeywordsPremiseExtant taxonRational planning modelEconomic planningEmpirical evidenceSelection (genetic algorithm)Process (computing)Political scienceEconomic growthPublic economicsEconomicsManagement

Abstract

fetched live from OpenAlex

The evidence regarding the extent of planning for local economic development decisions is mixed, and there is a general dearth of studies that examine whether rational planning is tied to policy choices in any systematic way. The research reported here rests on two premises. The first is the simple assumption that for planning to play an integral part in the economic development policy process there should be some empirical connection between local economic development goals—what officials want to achieve—and the economic development strategies they pursue. The second premise, based on extant literature, is that Canadian cities have different and more institutionally integrated planning traditions and practices than those in the USA, and hence should evidence a closer link between development goals and policies. Based on a survey of all of the municipalities in the USA and Canada with populations over 10 000, it is concluded that for US cities goals do not appear to correlate with discrimination among economic development policies; that is, planning is not related to the selection of activities. On the other hand, in Canada, cities' goals are related to specific policies in a more logical or rational fashion. In short, it appears that economic development planning is tied to the selection of policies to a greater extent in Canadian cities than in the USA.

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)0.000
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 score0.446
Threshold uncertainty score0.933

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 studies0.0010.000
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 head0.009
GPT teacher head0.231
Teacher spread0.221 · 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

Citations17
Published2006
Admission routes1
Has abstractyes

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