Canadian Speaker Session 10: Canada and U.S. Approaches to Cross-Border Sales of Pharmaceuticals - Canadian Speaker
Bibliographic record
Abstract
[First paragraph]: " Thanks very much, Sara. That was very helpful in setting the stage for my presentation, which is on the Canadian perspective on cross-border pharmaceutical trade, in particular on the rise of Internet pharmacies over the last several years. First, I want to thank you for inviting me to speak to you today. I am really honored to be here. I am going to review the background of the rise of Internet pharmacies and look at the regulatory framework in Canada that has allowed them to be profitable. I will look at the arguments for and against the pharmacies from the Canadian perspective, what the regulators are doing, and what they may do in the future to curtail the growth of Internet pharmacies."
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 | 0.005 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.017 | 0.004 |
| Scholarly communication | 0.013 | 0.004 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.022 | 0.014 |
| Insufficient payload (model declined to judge) | 0.123 | 0.015 |
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".