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Public hearing procedure in the management of city development: analysis of the world experience

2018· article· en· W2904769733 on OpenAlexaboutno aff
Zinaida Ivanova, Нина Данилина

Bibliographic record

VenueMATEC Web of Conferences · 2018
Typearticle
Languageen
FieldSocial Sciences
TopicLegal and Policy Issues
Canadian institutionsnot available
Fundersnot available
KeywordsLegislationPublic participationLegislaturePublic administrationGovernment (linguistics)Public relationsPolitical scienceContext (archaeology)DemocracySustainable developmentPoliticsLaw

Abstract

fetched live from OpenAlex

In recent decades, public participation in the management of urban development programs has become one of the most important aspects for the sustainable development of the society. The article focuses on public hearing method that aims to extend citizen involvement in the management of the community. The authors consider the ways of organizing and holding public hearings and public discussions examine the effective factors of the international experience, analyze legislative and regulatory documents, and study different conduct procedures and methodological materials related to the topic. The study investigates the comparative analysis of the procedures for conducting public hearings in Russia, Australia, the United States, Canada, Denmark, and India. The authors arrive to the conclusion that each country has developed specific context-based procedure strategies for citizen participation in discussing important social problems that depends on actual social development, democracy level and the forms of local self-government. The study identifies the shortcomings of the Russian legislation on public hearings and public discussions in that may lead to the lack of citizen participation in management urban affairs and therefore requires improvement using positive experience of other countries.

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)0.006
metaresearch head score (Gemma)0.008
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 score0.008
Threshold uncertainty score0.034

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0060.008
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0020.003
Science and technology studies0.0080.008
Scholarly communication0.0060.005
Open science0.0010.006
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 head0.120
GPT teacher head0.358
Teacher spread0.238 · 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

Citations2
Published2018
Admission routes1
Has abstractyes

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