Bibliographic record
Abstract
Most Internet users receive unsolicited invitations to enhance their health through the purchase of online medications. Often these medications are illegal and may even be counterfeit. However, there are a few legitimate online pharmacies. The National Association of Boards of Pharmacy has established the Verified Internet Pharmacy Practice Sites program, which certifies the legitimacy of some Internet merchants. Also, there are hundreds of Canadian pharmacies online because of the rise in popularity of Canadian drugs. The actual number of online Canadian pharmacies is difficult to estimate, and many of the so-called Canadian pharmacies are not from Canada. Besides the few legitimate sites in the United States and Canada, most online pharmacies deal with unapproved, illegal, and counterfeit medication. It is hard to know the number of online pharmacies because of the complex structure of the Internet. Their rapid growth can mainly be attributed to huge profits, but online pharmacies are also used for money laundering and may be used for terrorism. Although the United States has been limited in its actions, it still has taken numerous measures. However, internationally, online pharmacies do not appear to be as much of a problem, so almost any action taken has been led by the United States.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.003 | 0.012 |
| Scholarly communication | 0.008 | 0.007 |
| Open science | 0.000 | 0.003 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.014 | 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".