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Record W3209636922 · doi:10.32920/ryerson.14660784.v1

Willful ignorance as resistance, harm reduction workers and ruling relations

2021· preprint· en· W3209636922 on OpenAlexaff
Christopher Maxim Piercey Dalton

Bibliographic record

Venuenot available
Typepreprint
Languageen
FieldMedicine
TopicHIV, Drug Use, Sexual Risk
Canadian institutionsCarleton UniversityToronto Metropolitan UniversityCentre for Social Innovation
FundersAustralian Government
KeywordsHarm reductionIgnoranceAgency (philosophy)HarmPublic relationsPoliticsResistance (ecology)Corporate governancePolitical scienceSociologyBusinessMedicineLawNursingSocial sciencePublic health

Abstract

fetched live from OpenAlex

This paper will explore how front-line harm reduction workers govern the space of agency services. In order to study how this is done this writer completed an institutional ethnography to illuminate how power operates in the day-to-day practice of a harm reduction agency. Harm reduction services have been criticized as a site of neoliberal governance through risk-management. This study aims to explore how harm reduction workers perform and understand their role within their agency. This writer interviewed front-line staff members that distribute harm reduction material, asking them about their adherence to their organizational policies and procedures. The policies represented by the text of the signage within agencies was also analyzed. Study results showed that staff members used wilful ignorance to allow people to use drugs on agency premises, provided they did so in a discreet manner. Harm reduction workers also tried to reduce the suffering, and promote the larger political goals of harm reduction, to help people who use drugs.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.015
metaresearch head score (Gemma)0.017
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.017
Threshold uncertainty score0.080

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0150.017
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0020.001
Science and technology studies0.0160.120
Scholarly communication0.0170.010
Open science0.0010.010
Research integrity0.0050.007
Insufficient payload (model declined to judge)0.0050.000

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 head0.040
GPT teacher head0.341
Teacher spread0.301 · 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 source (direct Gemma or distilled Codex), 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
Published2021
Admission routes1
Has abstractyes

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