MétaCan
Menu
Back to cohort

Urban Agglomeration as an Element of Regional Policy (Canada example)

2016· article· en· W2517457565 on OpenAlexaboutno aff
Pavel Stroyev, M. N. Rawaiev

Bibliographic record

VenueRussian Journal of Industrial Economics · 2016
Typearticle
Languageen
FieldSocial Sciences
TopicCross-Border Cooperation and Integration
Canadian institutionsnot available
Fundersnot available
KeywordsElement (criminal law)Economic geographyEconomies of agglomerationUrban agglomerationRegional scienceRegional policyGeographyEconomicsPolitical scienceEconomic growth

Abstract

fetched live from OpenAlex

The transition from the alignment of regional development policy levels to the polarized politics of development leads to the task of forming a new system of strategic objectives of territorial development and mechanisms to achieve them. This agglomeration acting as «locomotives» of economic and social development for the surrounding areas can become centers for the modernization and development of the entire national economy. The article considers the role of urban centers in the formation of a federal state regional policy on the example of Canada. The paper analyzes the causes and consequences of the emergence of urban agglomerations, administrative and political structure of emerged subjects, considers entities organized on the basis of public / municipal-private partnerships, focusing on the implementation of cluster initiatives in the territory of agglomerations. On the example of metropolitan areas of Toronto and Montreal, features of formation of a control system and the development of urban centers in Canada are highlighted. The work reveals the positive and negative sides of the agglomeration municipalities, a comparative analysis of the characteristics of agglomerate in Russia and Canada is presented, approaches are proposed. The presented Canadian experience shows that in formation of urban agglomeration it is necessary to seek a compromise between integration and individualization.

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)0.000
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 score0.960
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0000.000
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 head0.057
GPT teacher head0.319
Teacher spread0.262 · 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 designNot applicable
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
Published2016
Admission routes1
Has abstractyes

Explore more

Same venueRussian Journal of Industrial EconomicsSame topicCross-Border Cooperation and IntegrationFrench-language works237,207