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Record W2325682412 · doi:10.4172/2155-6105.1000140

A Critical Examination of Experiential Knowledge in Illicit Substance Use Research and Policy

2013· article· en· W2325682412 on OpenAlexaboutno aff
Lauren Casey

Bibliographic record

VenueJournal of Addiction Research & Therapy · 2013
Typearticle
Languageen
FieldHealth Professions
TopicMental Health and Patient Involvement
Canadian institutionsnot available
Fundersnot available
KeywordsExperiential learningExperiential knowledgeMeaning (existential)Inclusion (mineral)ReflexivityPublic relationsKnowledge managementSociologyPsychologyPolitical scienceSocial psychologySocial scienceEpistemologyPedagogyComputer sciencePsychotherapist

Abstract

fetched live from OpenAlex

This paper examines the role of experiential user group knowledge in the development of substance use research, policy, and programming. Specifically drawing on themes contained within the sociology of knowledge and Marxist conflict theory, it is argued that the inclusion of experiential persons and collectivities is necessary to produce socially and culturally grounded knowledge regarding the meaning and consequences of illicit substance use. We argue that knowledge flowing from the everyday existence and experiences of drug users forms the basis of effective policy and programming. Experiential knowledge challenges “expert” groups such as policy makers and academic researchers to be reflexive about their position vis-à-vis those they study and to develop effective alliances with user groups. In addition to informing research, policy and programming, user group organizations play a key role in challenging dominant rhetoric regarding illicit drug use as a social problem and demonstrate that the most marginalized members of society can effectively mobilize in the interest of emancipatory social change. Despite the important benefits associated with the inclusion of experiential persons and user groups in research and various levels of social policy and programming, many structural and cultural barriers to meaningful inclusion of user groups exist; it is important to identify these barriers so that they can be strategically engaged and overcome. Two Canadian examples of user group organizations are discussed in order to illustrate both the positive influence and common challenges associated with including experiential knowledge and user group organizations in policy and program development.

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)0.008
metaresearch head score (Gemma)0.001
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 score0.580
Threshold uncertainty score0.915

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0080.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0010.000
Scholarly communication0.0000.001
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0010.000

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 head0.517
GPT teacher head0.583
Teacher spread0.065 · 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
Published2013
Admission routes1
Has abstractyes

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