THE CAUSE OF THE AUSTRALIAN HEROIN SHORTAGE: TIME TO RECONSIDER?
Bibliographic record
Abstract
are available in more disaggregated form than annual totals, a re-analysis of those data using time-series methods would yield two advantages: we would gain an understanding of trends and autocorrelation structures in the data, and short-term predictive relationships between the indicators could be identified.In the longer term, when and if time-series data on aspects of heroin and other drug markets in Australia and Canada are available in sufficiently disaggregated form, an excellent initiative would be to combine them in an international database.That would enable researchers to directly (and collaboratively) address questions about how these markets are interrelated and whether their internal dynamics are driven by the same influences.We may then know a little more.
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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 | 0.023 | 0.126 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.005 | 0.010 |
| Scholarly communication | 0.006 | 0.018 |
| Open science | 0.008 | 0.005 |
| Research integrity | 0.030 | 0.043 |
| Insufficient payload (model declined to judge) | 0.008 | 0.005 |
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".