A Critical Examination of Experiential Knowledge in Illicit Substance Use Research and Policy
Bibliographic record
Abstract
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.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.095 | 0.096 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.004 |
| Science and technology studies | 0.027 | 0.127 |
| Scholarly communication | 0.018 | 0.021 |
| Open science | 0.003 | 0.021 |
| Research integrity | 0.007 | 0.014 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".