A case study of the DULF compassion club and fulfillment centre—A logical step forward in harm reduction
Bibliographic record
Abstract
In 2022, the Drug User Liberation Front's Compassion Club and Fulfillment Centre emerged as a groundbreaking initiative and research endeavor aimed at addressing the alarming rise in overdose deaths within Vancouver's Downtown Eastside. As the first of its kind, this pioneering model operated as a non-profit, low-barrier, and non-medicalized approach to regulating the volatility of the content of the illicit drug market in order to prevent overdose deaths. Going beyond traditional overdose prevention methods, the Drug User Liberation Front's Compassion Club and Fulfillment Centre not only provided supervised consumption services, but also supplied rigorously tested cocaine, heroin, and methamphetamine at cost to club members. This intrinsic case study offers a unique perspective on the operation of Drug User Liberation Front's Compassion Club and Fulfillment Centre, delving into its inception, development, implementation, and the challenges it faced in its operation. Ultimately, the insights garnered from the Drug User Liberation Front's Compassion Club and Fulfillment Centre hold significant value for others interested in establishing similar programs or exploring de-medicalized approaches regulating substances in order to prevent overdose deaths.
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 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 | 0.002 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.018 | 0.007 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.003 | 0.005 |
| Research integrity | 0.006 | 0.007 |
| Insufficient payload (model declined to judge) | 0.008 | 0.001 |
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".