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Record W2131233558 · doi:10.1177/2158244013519363

Impediments to Information and Knowledge Sharing Within Policing

2014· article· en· W2131233558 on OpenAlexaboutno aff
Douglas Edward Abrahamson, Jane Goodman‐Delahunty

Bibliographic record

VenueSAGE Open · 2014
Typearticle
Languageen
FieldSocial Sciences
TopicKnowledge Management and Sharing
Canadian institutionsnot available
FundersBureau of Justice Assistance
KeywordsInformation sharingOrganizational cultureInformation overloadKnowledge managementOrganizational structurePublic relationsKnowledge sharingQualitative researchBusinessPerspective (graphical)Organizational learningSociologyPolitical scienceComputer science

Abstract

fetched live from OpenAlex

Information sharing is the lifeblood of policing, yet information/knowledge sharing within and across organizations remains problematic. This article elaborated on previous research on organizational information culture and its impact on information use outcomes in policing by examining perceived impediments to information sharing of 134 officers in three Canadian police organizations. Inductive qualitative analysis of an open-ended question revealed seven mutually exclusive impediment themes: processes/technology, individual unwillingness, organizational unwillingness, workload/overload, location/structure, leadership, and risk management. When viewed from the knowledge management infrastructure perspective, organizational structure was the single most common impediment identified, followed closely by organizational culture. Each organization had unique constellations of information sharing impediments. Recommendations for policy and practice are discussed.

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)0.014
metaresearch head score (Gemma)0.065
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 score0.063
Threshold uncertainty score0.125

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0140.065
Meta-epidemiology (narrow)0.0000.001
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0020.002
Science and technology studies0.0100.008
Scholarly communication0.0090.005
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 head0.029
GPT teacher head0.346
Teacher spread0.317 · 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

Citations28
Published2014
Admission routes1
Has abstractyes

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