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Record W3191262142 · doi:10.1108/dat-02-2021-0010

Sociological and spatial dynamics of an evolving Parisian open drug scene: the case of the “Colline du Crack”

2021· article· en· W3191262142 on OpenAlexaff
Candy Jangal, Mathieu Lovera, Sayon Dambélé, Marie Jauffret‐Roustide

Bibliographic record

VenueDrugs and Alcohol Today · 2021
Typearticle
Languageen
FieldMedicine
TopicHIV, Drug Use, Sexual Risk
Canadian institutionsBritish Columbia Centre on Substance Use
Fundersnot available
KeywordsOriginalitySociologySpace (punctuation)Dynamics (music)EthnographyAdvertisingAestheticsMedia studiesSocial scienceQualitative researchArtComputer scienceAnthropologyBusiness

Abstract

fetched live from OpenAlex

Purpose In November 2019, an open drug scene, commonly called “Colline du crack” and located in Paris was forcibly closed after 10 years of existence. This paper aims to understand how that space has evolved over the years to become a major hub for drug use. Design/methodology/approach The authors used a qualitative approach that included interviews with 52 people who use drugs (PWUD) and 54 field professionals and ethnographic observations. The authors asked questions about the evolution of the major sites of crack visibility in Paris and about social representations related to these spaces. They compared their datas with datas drawn from gray literature. Findings La Colline emerged on an isolated slope, away from police repression and local anti-crack organizations. In the beginning, it was a discrete, communal space regulated by PWUD. Starting in 2015, social transformations in the neighborhood turned la Colline into a central hub for dealing and using crack. La Colline became an open scene which led to its evacuation in 2019. Originality/value This paper contributes to literature on community building of drug consumers. The authors are also using a wide variety of methodological tools.

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 categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.558
Threshold uncertainty score0.342

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
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 head0.033
GPT teacher head0.339
Teacher spread0.306 · 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 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

Citations4
Published2021
Admission routes1
Has abstractyes

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