The contribution of research to the development of a national cannabis policy in Australia
Bibliographic record
Abstract
AIMS: To describe the formulation of a National Cannabis Strategy in Australia in 2006 and discuss the contribution that research evidence has made to its development. METHODS: A description of trends in cannabis use in Australia and policy responses to it from the early 1970s to the present, with an analysis of the evidence and arguments that have been used to support competing policies towards cannabis use. RESULTS: In 1977 an Australian Senate Committee recommended that a national cannabis policy should be developed that removed criminal penalties for personal possession and use. The arguments for the recommendation echoed those used to support similar earlier recommendations made in Canada, the Netherlands, the United Kingdom and the United States in the late 1960s and early 1970s. They were reiterated by a National Cannabis Task Force in 1994, but a national cannabis strategy was not developed until 2006. By default, the strategy supported continued prohibition on the production and sale of cannabis, and left penalties for cannabis use to state governments. It advocated public education campaigns to discourage young people, especially those at high risk, from initiating cannabis use, and to reduce the progression to regular use among young people who had used cannabis. It also supported efforts to reduce the availability of cannabis and to improve treatment for problem cannabis users. The research evidence which was cited as motivating the development of a national cannabis policy was epidemiological data on rising rates of use among young people, and emerging evidence from longitudinal studies in Australian and New Zealand that some adolescent cannabis users experienced harm as a result of their use. CONCLUSIONS: The impact that research evidence has had on Australian cannabis policy over the past three decades has, as in many comparable countries, been constrained by the limited policy options under consideration, and by the fact that interpretation of the evidence has been contested by advocates of the limited policy options under debate.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".