Buying and Selling Prescription Drugs on the Internet:
Bibliographic record
Abstract
In recent years, Internet pharmacies have emerged in Canada, primarily to supply US customers with lower-cost drugs. The pharmacies use e-commerce and other technologies to reach large numbers of potential customers and to exploit the sizeable price difference that exists for prescription drugs in the two countries. The resulting cross-border trade in drugs grew rapidly from 2001.2 Despite rapid, short-term success, the survival of Internet pharmacies was not assured. Critics of Internet pharmacies claimed that their practices were illegal, unethical, and unsafe. Further, the traditional pharmaceutical supply chain had reacted strongly to the challenge laid down by Internet pharmacies. The future position of Internet pharmacies was therefore uncertain. The case study examines the Internet pharmacy industry, with one company (Mediplan Health Consulting Inc.) providing illustrations. It shows that e-commerce technology was critical for Internet pharmacies at start-up, but that non-technical matters quickly became more important management issues.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.007 | 0.009 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.003 | 0.001 |
| Insufficient payload (model declined to judge) | 0.010 | 0.001 |
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".