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Record W4297120376 · doi:10.1016/j.drugpo.2022.103857

Child-centred harm reduction

2022· article· en· W4297120376 on OpenAlexaff
Damon Barrett, Claudia Stoicescu, Meaghan Thumath, Emma Maynard, Russell Turner, Sam Shirley‐Beavan, Eliza Kurcevič, Frida Petersson, Jennifer Hasselgård-Rowe, Corina Giacomello, Ella Wåhlin, Rick Lines

Bibliographic record

VenueInternational Journal of Drug Policy · 2022
Typearticle
Languageen
FieldMedicine
TopicPrenatal Substance Exposure Effects
Canadian institutionsUniversity of British Columbia
FundersForskningsrådet om Hälsa, Arbetsliv och Välfärd
KeywordsHarm reductionHarmIntersection (aeronautics)Field (mathematics)Work (physics)PsychologyCriminologySociologyPolitical sciencePublic relationsLawMedicineNursingPublic healthEngineering

Abstract

fetched live from OpenAlex

Harm reduction has become increasingly influential in drug policy and practice, but has developed primarily around adult drug use. Theoretical, practical, ethical and legal issues pertaining to children and adolescents under the age of majority - both relating to their own use and the effects of drug use among parents or within the family - are less clear. This commentary proposes a sub-field of drug policy at the intersection of harm reduction and childhood which we refer to as 'child-centred harm reduction'. We provide a definition and conceptual model, as well as illustrative questions that emerge through a child-centred harm reduction lens. Many people in different countries are already working on these kinds of issues, whose work needs greater recognition, analysis and support. In beginning to name and define this sub-field we hope to improve this situation, and inspire further international debate, collaboration, and innovation.

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: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.230
Threshold uncertainty score0.425

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 studies0.0000.000
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 head0.011
GPT teacher head0.292
Teacher spread0.281 · 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 designNot applicable
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

Citations11
Published2022
Admission routes1
Has abstractyes

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