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Record W2326167037 · doi:10.1093/police/pas010

Sen, Sankar (2010). * ENFORCING POLICE ACCOUNTABILITY THROUGH CIVILIAN OVERSIGHT

2012· article· en· W2326167037 on OpenAlexaff
P. F. McKenna

Bibliographic record

VenuePolicing A Journal of Policy and Practice · 2012
Typearticle
Languageen
FieldSocial Sciences
TopicCorruption and Economic Development
Canadian institutionsDalhousie University
Fundersnot available
KeywordsAccountabilityPolitical scienceDemocracySubject (documents)CommissionHuman rightsPublic administrationLawSociologyPublic relationsPolitics

Abstract

fetched live from OpenAlex

The author, currently Senior Fellow at the Institute of Social Sciences, New Delhi has extensive experience as a member of the Indian Police Service, including a leadership role with the National Police Academy and has been affiliated with the National Human Rights Commission. All of this background provides an indication that the author comes to his subject with a combination of practical and theoretical experience. This publication attempts an international treatment of the broad topic of civilian oversight of policing and includes some detailed considerations in several jurisdictions. The book’s 15 chapters are devoted to observations that delve into the concept of police accountability. The author begins with reflections on the complications associated with policing in democratic societies. The thrust of Sen’s opening position is that democratic principles require police who are ‘accountable to multiple mechanisms’ (p. xiii). Immediately, however, it may be suggested that police organizations are actually responsible to people; their ‘professional’ colleagues, as well as, to their civilian governing authorities and not to the mechanisms in place that merely structure police oversight. Such mechanisms are merely the outward trappings of the actual core of accountability; the human dimension.

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)0.004
metaresearch head score (Gemma)0.004
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.931
Threshold uncertainty score0.852

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0040.004
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.000
Scholarly communication0.0000.003
Open science0.0000.000
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 head0.084
GPT teacher head0.406
Teacher spread0.322 · 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 designNot applicable
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
Published2012
Admission routes1
Has abstractyes

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