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Record W2918029239 · doi:10.1111/nin.12286

Harm reduction: Philosophical drivers of conceptual tensions and ways forward

2019· article· en· W2918029239 on OpenAlexaff
Sunny Jiao

Bibliographic record

VenueNursing Inquiry · 2019
Typearticle
Languageen
FieldMedicine
TopicHIV, Drug Use, Sexual Risk
Canadian institutionsUniversity of British Columbia
Fundersnot available
KeywordsHarm reductionHarmIdeologySociologyRelevance (law)Privilege (computing)EpistemologyConceptual frameworkPoliticsPerspective (graphical)Social psychologyPsychologyPolitical scienceSocial scienceLawMedicineComputer sciencePublic healthPhilosophyNursing

Abstract

fetched live from OpenAlex

Of the various debates surrounding harm reduction, a conceptual tension that perhaps has the most relevance for the provision of services is that of harm reduction as a technical solution versus a contextualized social practice. The aim of this paper was to examine this conceptual tension. First, the two perspectives will be presented through the use of examples. Second, philosophical drivers that serve to underpin and justify each perspective will be explicated at the level of the knowledge that we privilege; the ideologies that we subscribe to; and the interests that we stand to serve. In this paper, I argue that the existing tension between technical and social approaches to harm reduction is embedded within discord pertaining to ways of knowing, paradigms of inquiry, prevailing ideologies, and notions of harm and risk. Building on these sources of tension, I suggest a means of philosophical reconciliation between the two approaches and ways forward, namely through acknowledging multiple sources of knowledge, through embracing paradigmatic incommensurability, through considering alternative conceptions of people who use drugs as political subjects, through involving service providers and end-users in shared decision-making, and lastly through reaffirming people who use drugs as the intended beneficiaries of services.

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)0.000
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: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.407
Threshold uncertainty score0.503

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
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 head0.119
GPT teacher head0.370
Teacher spread0.251 · 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

Citations15
Published2019
Admission routes1
Has abstractyes

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