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Questioning the use of ‘local democracy’ as a discursive strategy for political mobilization in Los Angeles, Montreal and Toronto

2003· article· en· W2107568424 on OpenAlexaffabout
Julie‐Anne Boudreau

Bibliographic record

VenueInternational Journal of Urban and Regional Research · 2003
Typearticle
Languageen
FieldSocial Sciences
TopicUrban Planning and Governance
Canadian institutionsYork University
Fundersnot available
KeywordsPoliticsDemocracyAutonomySociologyPolitical economyLocal governmentPolitical sciencePublic administrationLaw

Abstract

fetched live from OpenAlex

Between 1997 and 2002, homeowners in various parts of Los Angeles sought to secede from the City. At the same time, in Toronto, the province of Ontario forced the amalgamation of six municipalities forming a new megacity of 2.4 million. Residents mobilized for several months. In 2000, the province of Quebec forced the merger of 28 local municipalities in Montreal, forming a new city of 1.8 million. Angst came mostly from suburban Anglophone municipalities, where it was felt mergers would affect linguistic privileges. In the three cases, but stemming from different positions on the Left‐Right political spectrum, social actors claimed more local autonomy ‘in the name of local democracy’. Comparing these cases where institutional reforms and claims for local autonomy captured the political agenda, the article asks whether the use of ‘local democracy’ as a legitimizing tool for territorial claims may point to the emergence of a new generalized discursive strategy. Comparing variations in interpretations, and locating them in their respective local political cultures and in relation to the political positioning of claiming groups, highlights the processes by which socio‐political movements mobilize residents to their cause while avoiding accusations of NIMBYism. In the end, the article questions the moral tone attached to the expression ‘local democracy’.

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)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.686
Threshold uncertainty score0.996

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
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 head0.134
GPT teacher head0.423
Teacher spread0.289 · 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 designTheoretical or conceptual
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

Citations45
Published2003
Admission routes2
Has abstractyes

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