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Record W1982693036 · doi:10.3109/10826084.2012.644110

Collective Empowerment While Creating Knowledge: A Description of a Community-Based Participatory Research Project With Drug Users in Bangkok, Thailand

2012· article· en· W1982693036 on OpenAlexafffund
Kanna Hayashi, Nadia Fairbairn, Paisan Suwannawong, Karyn Kaplan, Evan Wood, Thomas Kerr

Bibliographic record

VenueSubstance Use & Misuse · 2012
Typearticle
Languageen
FieldMedicine
TopicHIV, Drug Use, Sexual Risk
Canadian institutionsUniversity of British ColumbiaSt. Paul's Hospital
FundersCanadian Institutes of Health Research
KeywordsParticipatory action researchEmpowermentGeneral partnershipCitizen journalismCommunity-based participatory researchSociologyPublic relationsResearch centerPopulationMedical educationMedicinePolitical science

Abstract

fetched live from OpenAlex

In light of growing concerns regarding the ongoing drug war in Thailand and a lack of support for people who inject drugs in this setting, in 2008, we undertook a community-based participatory research project involving a community of active drug users at a peer-run drop-in center in Bangkok. This case study describes a unique research partnership developed between academic and active drug users and demonstrates that participatory approaches can help empower this vulnerable population while generating valid research. Further research is needed to explore ways of optimizing community-based participatory research methods when applied to drug-using populations.

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)0.019
metaresearch head score (Gemma)0.014
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: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.024
Threshold uncertainty score0.099

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0190.014
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.002
Science and technology studies0.0240.015
Scholarly communication0.0060.004
Open science0.0020.013
Research integrity0.0030.003
Insufficient payload (model declined to judge)0.0030.000

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 head0.340
GPT teacher head0.435
Teacher spread0.095 · 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

Citations43
Published2012
Admission routes2
Has abstractyes

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