Bibliographic record
Abstract
Decisions regarding the legal status of cannabis have long been framed (for the public at least) with reference to the perceived health risks and harms associated with use. Yet, drug policy and legislation relating to the use of cannabis are rarely based on the scientific evidence of the known risks and harms. There are many reasons for this discrepancy, with the politicization of cannabis use, where ideology and moralizing are given precedence over the science, being one. Thus, we begin this research topic with Aggar-wal (1) discussion of how such politicization has contributed to the current smokescreen that is obscuring our understanding of cannabis, including the impact it has on the ability of researchers to collect and disseminate accurate information about the effects of cannabis use. The capacity of policy makers and legislators to develop evidence-based cannabis policies and laws is also contingent on researchers explaining the existing evidence, disseminating new research findings, and collaborating with relevant people, agen-cies, and government departments to improve the premises on which they base their policies and legislation. Roffman (2), who took this path through his involvement in the development of the legislation to legalize cannabis use in Washington State, provides an insider’s view of the processes and deliberations. While we will have to wait for the evaluation of this carefully designed model for regulating cannabis use, the following two articles provide some insight into patterns of cannabis use in contexts were consumption is relatively normalized. There are many parallels evident in the findings of Mostaghim and Hathaway (3) qualitative exploration of cannabis use among Canadian university students and Liebregts et al. (4) prospective investigation of cannabis use by young adults
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 | 0.001 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.009 | 0.004 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.004 | 0.004 |
| Insufficient payload (model declined to judge) | 0.816 | 0.755 |
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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".