“If We’re Going to do Anything, We Have to Do It Ourselves” - The Case for Drug User Self-Management
Bibliographic record
Abstract
This research project looks at the (self-)management strategies of drug users amidst the hostile socio-political environment manifested through 100 years of prohibition. This thesis focuses on Vancouver, B.C., Toronto, Ontario and various Canadian regions, analysing how current measures of policy enacted to address the worsening drug poisoning crisis have not been sufficient considering the crisis' death toll. An examination of strategies of drug user (self-)management is illustrated through a study of different strategies of drug user (self-)management, examining moments of drug user self-management that illustrate that drug users are not an ambivalent, apolitical group, but rather a highly radical, organised and effective political force that has forced drug policy forward for the last 30+ years. Further, this thesis will examine and interrogate literature surrounding limitations to harm reduction implementation, problematization procedures, and the current literature on self-management. This thesis works to legitimise and celebrate the revolutionary work of drug users throughout Canada.
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 | 0.008 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.030 | 0.067 |
| Scholarly communication | 0.013 | 0.006 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.004 | 0.009 |
| Insufficient payload (model declined to judge) | 0.003 | 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".