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Record W2725747950 · doi:10.5334/sta.491

Is There Anybody There? Police, Communities and Communications Technology in Hargeisa

2017· article· en· W2725747950 on OpenAlexvenueno aff
Alice Hills

Bibliographic record

VenueStability International Journal of Security and Development · 2017
Typearticle
Languageen
FieldSocial Sciences
TopicPolicing Practices and Perceptions
Canadian institutionsnot available
Fundersnot available
KeywordsInformation and Communications TechnologyPublic relationsCommunity policingPerspective (graphical)Community engagementLocal communityPolitical scienceSociologyBusinessLawComputer science

Abstract

fetched live from OpenAlex

This article addresses the connection between information and communications technology (ICT) and police-community engagement in environments characterised by high access to mobile telephones but minimal police response rates. It examines public responses to a text alert project in Somaliland’s capital Hargeisa in order to explore the everyday choices shaping low-level police-community engagement. Although the project failed (local people did not use mobiles to alert the police to security issues requiring attention), it offers contextualised insights into both the specifics of daily police-community relations and the use of mobiles as a two-way technology capable of reaching low-income or marginalised populations in relatively safe urban environments. In focusing on how local expectations are, rather than should be, fulfilled, it finds little evidence to suggest that access to ICT leads to more responsive or accountable policing. For police, activities are shaped as much by community expectations as by the technologies available, and local preferences can offset the availability of globalised ICT. From this perspective, the key to understanding police-community engagement is found in the knowledge, skills and resources police need to fulfil local expectations, rather than the expectations of international donors.

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: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.190
Threshold uncertainty score0.980

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 studies0.0010.001
Scholarly communication0.0000.000
Open science0.0010.000
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 head0.106
GPT teacher head0.415
Teacher spread0.309 · 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 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
Published2017
Admission routes1
Has abstractyes

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