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Record W7023847610

Policing hip hop:
\nPolice and Other Agents of Control in Montreal Night Clubs

2023· dissertation· en· W7023847610 on OpenAlexaboutno aff

Bibliographic record

VenueSpectrum Research Repository (Concordia University) · 2023
Typedissertation
Languageen
FieldSocial Sciences
TopicNight-time city culture
Canadian institutionsnot available
Fundersnot available
KeywordsClubEnforcementWhite (mutation)Law enforcementPolice brutality
DOInot available

Abstract

fetched live from OpenAlex

Policing hip hop:
\nPolice and Other Agents of Control in Montreal Night Clubs
\nAnthony Mclachlan
\nHip hop has been a part of urban spaces in the Global North and abroad for quite some time. It began in the predominantly Black South Bronx in 1973. Since then, it has spread across the United States and is arguably the most commercially successful genre of music currently existing. Through the four elements of hip hop – graffiti, breakdancing, DJing, and rapping – Black American youth conveyed their life experience to a broader public. Almost immediately after that was a response by law enforcement to contain this Black expressive artform. This research examines how hip hop is policed in Montreal nightclubs. Focusing on the actions of the police, club bouncers, and other club staff, it reveals the various methods and logics that shape the nightclub environment on hip hop nights, as well as the differences between the policing of Black and white hip hop nights. The findings suggest that anti- blackness in Montreal and Quebec forges imagined associations between Black hip hop artists and fans and street gangs, suturing a perceived relationship between Black hip hop and criminality that invites and justifies excessive policing and a confined public experience for Black Montrealers.

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)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.126
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0020.002
Science and technology studies0.0010.001
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0010.001
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 head0.030
GPT teacher head0.319
Teacher spread0.289 · 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 designObservational
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 routes1
Has abstractyes

Explore more

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