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Record W1511169402 · doi:10.1108/01443330610657197

Central city socio‐economic characteristics and public participation strategies

2006· article· en· W1511169402 on OpenAlexaboutno aff
Robert Mark Silverman

Bibliographic record

VenueInternational Journal of Sociology and Social Policy · 2006
Typearticle
Languageen
FieldSocial Sciences
TopicPublic Policy and Administration Research
Canadian institutionsnot available
Fundersnot available
KeywordsScope (computer science)Voluntarism (philosophy)Public participationOriginalityLocal governmentGovernment (linguistics)Political sciencePublic relationsPublic consultationPublic administrationBusiness

Abstract

fetched live from OpenAlex

Purpose This article aims to examine the mechanisms used by municipalities to stimulate public participation and, in part, to argue that contrasts between the socio‐economic make‐up of central cities in the USA and Canada explain these divergent techniques. Design/methodology/approach The article is based on a survey of planning departments measuring the types of public participation strategies used by local governments. Findings The article's findings indicate that Canadian municipalities adopt a broader range of public participation techniques related to: voluntarism and public engagement, neighborhood and strategic planning, and e‐government. In contrast, the article's findings indicate that US municipalities are more likely to promote public participation through mechanisms such as annual community meetings and referendums on public issues. Research limitations/implications The conclusion of the article offers recommendations for expanding the scope of public participation and developing strategies that maximize citizen input in community development activities in both countries. Practical implications The survey was conducted to identify the scope of public participation techniques used by local governments in the Niagara region. One limitation of this methodology is that it does not gauge the effectiveness of the participation techniques used by local governments or the intensity of public engagement. However, the results from this study provide future researchers with a mechanism for focusing future analysis. Originality/value The findings can assist in identifying new directions for enhancing public participation in the USA and Canada.

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: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.716
Threshold uncertainty score0.490

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 studies0.0000.001
Scholarly communication0.0000.001
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 head0.062
GPT teacher head0.431
Teacher spread0.369 · 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

Citations13
Published2006
Admission routes1
Has abstractyes

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