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Record W4409197176 · doi:10.1080/10439463.2024.2411538

Urban disorder in democratic transitions: Ukraine’s municipal policing debates in comparative perspective

2025· article· en· W4409197176 on OpenAlexafffund
Matthew Light, Anne‐Marie Singh, Aaron Erlich, Oleh Feday

Bibliographic record

VenuePolicing & Society · 2025
Typearticle
Languageen
FieldSocial Sciences
TopicPolicing Practices and Perceptions
Canadian institutionsMcGill UniversityToronto Metropolitan UniversityUniversity of Toronto
FundersSocial Sciences and Humanities Research Council of Canada
KeywordsPerspective (graphical)DemocracyPolitical scienceCommunity policingPublic administrationCriminologySociologyPolitical economyPoliticsLaw

Abstract

fetched live from OpenAlex

The paper examines debates on police decentralisation in Ukraine since 2014, when Russia first attacked Ukraine and the country began a democratic transition, through February 2022, when the current full-scale invasion began. Although Ukraine’s constitution gives no policing authority to local governments, municipalities have created their own de facto local policing services, ‘municipal guards.’ In Ukraine, unlike nearly all other post-Soviet states, war and democratic transition have brought onto the political agenda debates about policing decentralisation for urban public order, incivilities, and property rights. Previous studies of post-authoritarian police reform have neglected such debates, focusing on criminal law and human rights. We argue that clarifying the role of local authorities in controlling public order is key to the success of police reform and should receive greater political and scholarly attention. Indeed, there is a strong case that ‘democratic policing’ in modern states requires some local control of public order.

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)0.003
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: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.064
Threshold uncertainty score0.128

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0020.004
Science and technology studies0.0130.013
Scholarly communication0.0080.004
Open science0.0010.008
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0020.000

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 head0.046
GPT teacher head0.409
Teacher spread0.362 · 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 designQualitative
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
Published2025
Admission routes2
Has abstractyes

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