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Record W2891828139 · doi:10.1017/eis.2018.17

When civilian control is civil: Parliamentary oversight of the military in Belgium and New Zealand

2018· article· en· W2891828139 on OpenAlexaff
Philippe Lagassé, Stephen M. Saideman

Bibliographic record

VenueEuropean Journal of International Security · 2018
Typearticle
Languageen
FieldSocial Sciences
TopicPolicing Practices and Perceptions
Canadian institutionsCarleton University
Fundersnot available
KeywordsLegislaturePrincipal (computer security)Strengths and weaknessesPolitical sciencePublic administrationCommunity policingFunction (biology)Control (management)Civil affairsCommand and controlPublic relationsLawManagementEngineeringComputer securityEconomicsPsychologyComputer science

Abstract

fetched live from OpenAlex

Abstract This study introduces a new type of oversight in civil-military and executive-legislative relations:community policing. Building on principal-agent theory, this type of oversight emphasises trust rather than confrontation. To illustrate how community policing functions, the study examines how legislative oversight of military affairs operates in Belgium and New Zealand. Legislative defence committees in both countries rely on trust when overseeing the executive’s handling of defence affairs. This allows these committees to perform their oversight function at low cost in terms of time and effort, but with a high degree of access to information. Community policing therefore combines the strengths of recognised ‘police patrol’ and ‘fire alarm’ oversight, while avoiding their respective weaknesses. However, since it relies on a higher degree of trust and cooperation between the principal and agent, community policing is inherently fragile.

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)0.020
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 score0.447
Threshold uncertainty score0.890

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0060.020
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0040.006
Scholarly communication0.0040.002
Open science0.0010.003
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 head0.022
GPT teacher head0.298
Teacher spread0.276 · 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

Citations12
Published2018
Admission routes1
Has abstractyes

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