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Record W2139110314 · doi:10.1111/1468-2427.00277

Governance Restructuring in Los Angeles and Toronto: Amalgamation or Secession?

2000· article· en· W2139110314 on OpenAlexaffabout
Roger Keil

Bibliographic record

VenueInternational Journal of Urban and Regional Research · 2000
Typearticle
Languageen
FieldSocial Sciences
TopicUrbanization and City Planning
Canadian institutionsYork University
Fundersnot available
KeywordsSecessionRestructuringCorporate governanceMetropolitan areaPoliticsUrban politicsPolitical scienceDemocratizationPublic administrationDemocracyPolitical economyEconomySociologyGeographyLawEconomicsArchaeologyManagement

Abstract

fetched live from OpenAlex

Towards the end of the 1990s, a perplexing situation occurred in two large North American cities. In Toronto, Ontario, and Los Angeles, California, conservative political forces undertook to restructure the system of urban governance. While initiated by conservatives in both cases, in Toronto the result was consolidation; in Los Angeles secessionism is rampant. In both cases the political debate on amalgamation and secession is tied in with discourses on size, efficiency and form of urban government. In both cases, also, the shift from government to governance has been a central theme. This article investigates how local governance has changed in these two cities by comparing historical traditions of governance. Each city has a specific set of external relationships with other geographical and political scales and a set of characteristic internal contradictions. Internally, Los Angeles' tradition of splintered governance stands in contrast to Toronto's metropolitan governance model. Amalgamation and secession have been introduced as strategic options of governance restructuring in both cities in the late 1990s. Both (projected and realized) scalar changes of governance processes and institutions have been accompanied and characterized by social struggles and widespread political debate. The article outlines these debates and discusses the respective political alliances which have formed in both urban regions on the issue of amalgamation/secession. Vers la fin des années 1990, une situation troublante se produisit dans deux grandes villes d'Amérique du Nord: Toronto, en Ontario, et Los Angeles, en Californie. Les conservateurs y avaient entrepris de restructurer le système de gouvernance urbaine. Bien que l'initiative revienne aux mêmes forces politiques dans les deux cas, le résultat fut à Toronto une unification et à Los Angeles un sécessionnisme rampant. Dans les deux villes, le débat politique sur l'amalgamation et la sécession est lié aux discours sur la taille, l'efficacité et la forme d'un gouvernement urbain. De même, dans les deux villes, le passage de gouvernement à gouvernance s'est trouvé au centre des propos. Cet article recherche comment la gouvernance locale a évolué dans ces deux villes en comparant les traditions historiques. Chaque ville présente, d'une part, un ensemble particulier de relations extérieures avec d'autres échelles géographiques et politiques et, d'autre part, un ensemble de contradictions intérieures caractéristiques. Sur le plan intérieur, la tradition de gouvernance éclatée de Los Angeles s'oppose au modèle de gouvernance métropolitaine de Toronto. Vers la fin des années 1990, amalgamation et sécession ont été présentées dans les deux villes comme des options stratégiques pour la restructuration de la gouvernance. Les changements d'échelle –à la fois prévus et réalisés – concernant les processus et institutions de gouvernance ont été accompagnés et caractérisés par des conflits sociaux et un débat politique général. L'article expose ces discussions et aborde les alliances politiques respectives qui se sont formées dans les deux zones urbaines sur la question d'amalgamation/sécession.

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)0.003
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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.126
Threshold uncertainty score0.913

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.003
Science and technology studies0.0120.017
Scholarly communication0.0070.002
Open science0.0010.004
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0030.000

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 head0.068
GPT teacher head0.413
Teacher spread0.345 · 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

Citations120
Published2000
Admission routes2
Has abstractyes

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