Bibliographic record
Abstract
[Extract] Case study: The Demise of the Pan-pharmaceutical In mid-2003, the suspension, on the grounds of having uncovered evidence of serious product safety and quality breaches (TGA, 2003), of the manufacturing licence of Australasia's largest manufacturer of herbal, vitamin, and nutritional supplements resulted in a series of product recalls: some 1,800 products were withdrawn in Australia and 11500 in New Zealand, making this by far the largest product recall in Australasian history. The company Pan-Pharmaceuticals1 manufactured not only its own product range, but also contract-manufactured products for a large number of companies under a range of brand names. The bulk of the affected company's activities were based in the Australian market, with the organization supplying 40 per cent of the Australian complementary medicines market, however, 15 per cent of its total sales were in the New Zealand market and smaller quantities of Pan-manufactured stock were available in some 40 countries. The impact of the withdrawal was therefore felt in countries as diverse as Vietnam, the UK, and Canada. The unprecedented scale of the withdrawal across so many brands had the potential for contamination of the reputations not only of the companies whose products were produced by Pan, but also companies who did not use Pan in any way, and impacted on the complementary and alternative medicines category as a whole.
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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 | 0.001 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.004 | 0.002 |
| Scholarly communication | 0.008 | 0.005 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.007 | 0.003 |
| Insufficient payload (model declined to judge) | 0.252 | 0.081 |
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".