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Record W2303688879

광역 도시권 계획의 거버넌스 : 미국 샌프란시스코와 캐나다 밴쿠버에 대한 비교연구

2009· article· ko· W2303688879 on OpenAlexaboutno aff
신동호

Bibliographic record

Venue한국지역개발학회지 · 2009
Typearticle
Languageko
FieldSocial Sciences
TopicUrban Planning and Governance
Canadian institutionsnot available
Fundersnot available
KeywordsMetropolitan areaParticipatory planningCorporate governancePublic administrationAgency (philosophy)Urban planningBusinessLocal governmentRegional planningGovernment (linguistics)Economic growthPolitical scienceEnvironmental planningGeographySociologyFinanceEconomics
DOInot available

Abstract

fetched live from OpenAlex

This paper analyses the governance of metropolitan planning in two North American cities: San Francisco in the US and Vancouver in Canada. The paper argues that the core planning agency of the Vancouver region, GVRD, has established a strong status as a regional planning organization through a long period of participatory planning practice, leading an orderly coordination of interests among different municipalities and stake-holders; however, this is not the case for the San Francisco region. In the latter case, main roles of metropolitan planning, such as the provision of long-term vision, transit services. and clean water, have been fragmented among many different organizations, such as governments, quasi-, and non-government organizations, special districts, and private sectors. This fragmentation is believed to be a major source of the lack of efficacy, efficiency, and convenience in urban service provision of the region.

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)0.000
metaresearch head score (Gemma)0.001
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: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.467
Threshold uncertainty score0.940

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.002
Science and technology studies0.0020.003
Scholarly communication0.0020.001
Open science0.0000.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.018
GPT teacher head0.285
Teacher spread0.267 · 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
Published2009
Admission routes1
Has abstractyes

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