Supporting Future Cannabis Policy – Developing a Standard Joint Unit: A Brief Back-Casting Exercise
Bibliographic record
Abstract
The standardization of cannabis doses is a priority for research, policy-making, clinical and harm-reduction interventions and consumer security. Scientists have called for standard units of dosing for cannabis, similar to those used for alcohol. A Standard Joint Unit (SJU) would facilitate preventive and intervention models in ways similar to the Standard Drink (SD). Learning from the SD experiences allows researchers to tackle emerging barriers to the SJU by applying modern forecasting methods. During a workshop at the Lisbon Addictions Conference 2019, a back-casting foresight method was used to address challenges and achieve consensus in developing an SJU. Thirty-two professionals from 13 countries and 10 disciplines participated. Descriptive analysis of the workshop was carried out by the organizers and shared with the participants in order to suggest amendments. Several characteristics of the SJU were defined: (1) core values: easy-to use, universal, focused on THC, accurate, and accessible; (2) key challenges: sudden changes in patterns of use, heterogeneity of cannabis compounds as well as in administration routes, variations over time in THC concentrations, and of laws that regulate the legal status of recreational and medical cannabis use); and (3) facilitators: previous experience with standardized measurements, funding opportunities, multi-stakeholder support, high prevalence of cannabis users, and widespread changes in legislation. Participants also identified three initial steps for the implementation of a SJU by 2030: (1) Building a task-force to develop a consensus-based SJU; (2) Expanded available national-level data; (3) Linking SJU consumption to the concept of "risky use," based on evidence of harms.
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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 | 0.105 | 0.068 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.015 | 0.009 |
| Scholarly communication | 0.012 | 0.019 |
| Open science | 0.006 | 0.024 |
| Research integrity | 0.018 | 0.020 |
| Insufficient payload (model declined to judge) | 0.012 | 0.003 |
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".