Bibliographic record
Abstract
Dear Editor, It was with great interest that I read Bernard Rollin’s response to the ethical question of the month on duplicity in pet food marketing, published in the August issue (Can Vet J 2011;52:824–825). The pharmaceutical industry has for decades (excepting Quebec) sold duplicate products both through the ethical market, that is, by veterinarians, and by over-the-counter (OTC) outlets. In some situations, products such as Ivermectin were first marketed only by veterinarians. One year later, the same product was available at virtually all OTC locations — at a greatly reduced price. Public perception of this marketing approach was that veterinarians were selling products at over-inflated prices, and reaping excessive profits. The cost of doing business for OTC outlets (that is, overhead) is much less than that of veterinary hospitals — hence the need for higher mark-ups by veterinarians. This has led somewhat to the “erosion of trust” which Dr. Rollin refers to. I would encourage readers to check out www.farmersfarmacy.com for themselves and compare this to Quebec’s “vet only” sale of livestock medications.
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 | 0.007 | 0.037 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.006 | 0.005 |
| Scholarly communication | 0.005 | 0.007 |
| Open science | 0.004 | 0.002 |
| Research integrity | 0.072 | 0.067 |
| Insufficient payload (model declined to judge) | 0.006 | 0.005 |
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".