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The code of silence: Revisiting South African police integrity

2012· article· en· W7134267501 on OpenAlexaffabout
Sanja Kutnjak Ivković, Adri Sauerman

Bibliographic record

VenueSouth African Crime Quarterly · 2012
Typearticle
Languageen
FieldSocial Sciences
TopicPolicing Practices and Perceptions
Canadian institutionsCanadian Criminal Justice Association
Fundersnot available
KeywordsMisconductSeriousnessOfficerSilenceAgency (philosophy)Quarter (Canadian coin)Law enforcementCode (set theory)

Abstract

fetched live from OpenAlex

In exploring the contours of the code of silence among South African police officers, our 2005 survey of 379 police officers from seven provinces found that a substantial proportion of respondents were keen to protect various forms of police corruption. Between July 2010 and August 2011 we engaged in the second sweep of the survey, encompassing 771 police officers (commissioned and non-commissioned) from nine South African provinces. Our results provide further evidence of the presence of the code of silence covering various forms of police misconduct. At least one quarter of the respondents would protect a fellow officer who verbally abused citizens, covered up police driving under the influence (DUI) accident, accepted gratuities, or failed to react to graffiti. At least one out of eight police officers showed willingness to cover up internal corruption, striking a prisoner, a kickback, a false report on drug possession, and protection of a hate crime. The results further indicate that the respondents’ willingness to adhere to the code of silence is directly related to their estimates of whether other police officers in their agency would protect such behaviour with silence, as well as to their estimates of the seriousness of misconduct and expected discipline.

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)0.002
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesScience and technology studies
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.148
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
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 head0.065
GPT teacher head0.363
Teacher spread0.297 · 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.

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
Published2012
Admission routes2
Has abstractyes

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