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Record W561486586 · doi:10.4324/9781351317726

URBAN and REGIONAL Planning in Canada

2017· book· en· W561486586 on OpenAlexaboutno aff
J. Barry Cullingworth

Bibliographic record

Venuenot available
Typebook
Languageen
FieldSocial Sciences
TopicUrbanization and City Planning
Canadian institutionsnot available
Fundersnot available
KeywordsGeographyEnvironmental planningRegional planningRegional scienceUrban planningEngineeringCivil engineering

Abstract

fetched live from OpenAlex

Originally published in 1987, this book presents a wide-ranging review of urban, regional, economic, and environmental planning in Canada. A comprehensive source of information on Canadian planning policies, it addresses the wide variations between Canadian provinces. While acknowledging similarities with programs and policies in the United States and Britain, the author documents the distinctively Canadian character of planning in Canada.Among the topics addressed in the book are: the agencies of planning; on the nature of urban plans; the instruments of planning; land policies; natural resources; regional planning at the federal level; regional planning and development in Ontario; regional planning in other provinces; environmental protection; planning and people; and reflections on the nature of planning in Canada.The author documents how governmental agencies handle problems of population growth, urban development, exploitation of natural resources, regional disparities, and many other issues that fall within the scope of urban and regional planning. But he goes beyond this to address matters of politics, law, economics, social organization. The book is pragmatic, eclectic, interpretive, and critical. It is a valuable contribution to international literature on planning in its political context.

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)0.001
metaresearch head score (Gemma)0.002
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Other · Consensus signal: Other
Teacher disagreement score0.081
Threshold uncertainty score0.589

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0020.009
Science and technology studies0.0090.005
Scholarly communication0.0090.002
Open science0.0010.003
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0250.005

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 head0.045
GPT teacher head0.278
Teacher spread0.233 · 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 designNot applicable
Domainnot available
GenreOther

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

Citations37
Published2017
Admission routes1
Has abstractyes

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