HEROIN SUPPLY IN THE LONG‐TERM AND THE SHORT‐TERM PERSPECTIVES: COMMENTS ON WOOD <i>ET AL</i>. 2006
Bibliographic record
Abstract
Wood et al. (2006) tackle an important question. Much has been made of the 2001 heroin drought in Australia. It has been asserted, without much documentation, that other markets supplied by Golden Triangle countries were not affected. Using data on four series, the authors claim that ‘Canada appears to have experienced a similar reduction in heroin availability without augmenting or altering supply reduction efforts’ which appears ‘to contradict the conclusion that the Australian heroin shortage was an isolated global phenomena resulting from domestic law enforcement efforts’. The explanation for the Australian drought is indeed hard to determine. Degenhardt et al. (2005) made an attempt to deduce the factors involved. They concluded that ‘[t]he shortage was due most probably to a combination of factors that operated synergistically and sequentially’. All the factors identified were on the supply side and they were Australia-specific, although that included law enforcement operations in Asia targeting Australian exporters. It is fair to say that the results are best regarded as speculative, particularly as there are few instances in which drug law enforcement can claim more than transitory success. Finding that the same shortage occurred in another unrelated market would be important evidence against a claim that Australian law enforcement was the principal cause of the drought there. The Wood et al. analyses are suggestive, but not convincing, because they do not distinguish between long-term trends and the abrupt reduction in supply that is what is thought of typically as the Australian ‘drought’. Their approach was straightforward. For each data series they examined differences between the period before and after 2001. With only six data points for three of the series [British Columbia (BC) overdose deaths, Vancouver naloxone episodes and BC heroin seizures] there was no possibility of statistical tests. The differences were large, however, between one-third and two-thirds and thus, at first blush, persuasive. Yet Wood et al.'s own data suggest that the decline started well before 2001. The overdose deaths fell steadily from the first year of observations, 1998, onwards. Seizures would have fallen as well, but for a single very large seizure (100 kg) in September 2000. Naloxone episodes peaked in 1998–99. For the higher frequency and longest series, frequency of daily heroin injection by Vancouver injecting drug users (IDU), the authors reported only that there is a difference between pre- and post-2001. They did not report whether 2001 is the optimal break-point; an analysis that used 1999 or 1998 as the break point or that applying a linear trend might have explained the results just as well. The importance of these details is that the Australian heroin drought was a very abrupt event set against a backdrop of long-term trends. Purity in Melbourne fell gradually but steadily from 70 to 75% in early 1999 to approximately 40% in late 2000, but then plummeted from an average of 39.6% in the last 2 weeks of December to 18.5% in the last 2 weeks of January (Moore et al. 2005). Overdoses declined similarly quickly, so it is not entirely clear whether Vancouver's 45% reduction in naloxone use over 3 years is really similar to Melbourne's 74% decline in just 3 months. More generally, multiple indicator data in Sydney and Melbourne show a very sharp decline in the first quarter of 2001. Thus the Australian shortage appears to have been associated with some event, not merely a tightening of the market in response, for example, to declining production in the Golden Triangle region. The British Columbia indicators, as analysed here, are consistent with quite a different story. The long-term tightening of the market may have begun well before 2001, been shared by Australia and Vancouver and been driven by broader market events without contradicting the idea that something dramatic and distinctive happened in Australian heroin markets within a very short time, an event that has come to be called the heroin ‘drought’. Understanding of the heroin shortage(s) would be strengthened by two kinds of research. First is more fine-grained statistical analyses of Canadian indicators, ideally with monthly if not bi-weekly data series. The second is an examination of what happened in other major markets served by the Golden Triangle producers. China reported a major increase in heroin seizures between 2000 (6281 kg) and 2001 (13 200 kg), which— subject to all the limitations of seizure data—does not parallel the Wood et al. seizure data (UNODC 2004).
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.002 |
| 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".