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Record W4394813363 · doi:10.1080/03057070.2023.2334188

‘We get sucked into everybody’s mess’: Protests and Public Order Policing in South Africa

2023· article· en· W4394813363 on OpenAlexafffund
Gary Kynoch

Bibliographic record

VenueJournal of Southern African Studies · 2023
Typearticle
Languageen
FieldSocial Sciences
TopicSouth African History and Culture
Canadian institutionsDalhousie University
FundersSocial Sciences and Humanities Research Council of Canada
KeywordsDissentCrowdsPolitical scienceHostilityMediationOrder (exchange)Element (criminal law)IncitementPublic orderState (computer science)Public relationsLawCriminologySociologyPoliticsBusinessSocial psychologyPsychologyComputer security

Abstract

fetched live from OpenAlex

Based on interviews with 43 serving members at four Public Order Police units, this article highlights the perspectives of officers involved in arguably the most contentious and visible aspect of South African policing. As the primary arm of the police responsible for crowd management, Public Order members are at the coalface of a country inundated with protests. Respondents describe an environment in which they are forced to contend with politicians’ negligence and incitement of protest, the vagaries of mediation and the hostility of many protestors, all while they often share the frustrations of the crowds they are required to manage. Examining these representations provides critical insight into how these police, who are a central component of the civil strife afflicting South Africa, interpret the challenges of the job. Public Order Police are an integral element in protests but not just as the strong arm of the state suppressing dissent. Rather, they fulfil multiple functions in an intricate protest landscape over which they exercise limited control.

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)0.002
Version: codex-gemma-dda1882f352aValidation 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 score0.031
Threshold uncertainty score0.664

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.002
Science and technology studies0.0010.001
Scholarly communication0.0000.000
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 head0.054
GPT teacher head0.310
Teacher spread0.256 · 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 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
Published2023
Admission routes2
Has abstractyes

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