Duplicity in pet food marketing--a comment.
Bibliographic record
Abstract
Dear Editor, Ethicist Bernard Rollin’s commentary on “duplicity in pet food marketing” (Can Vet J 2011;52:824–826) is very disturbing, not only on this morally questionable practice by a veterinary pet food company, but also on the erosion of the trust by the public towards veterinary practitioners, veterinary colleges, and our associations. The act of “well-publicized financial support by pet food companies to veterinary colleges and professional associations” from such a company implicates veterinary practices that sell the food, our colleges, and associations with this morally questionable practice. As Dr. Rollin discusses, this can easily be interpreted as “kickbacks to the profession” leading to a “significant loss of public confidence in the veracity and integrity of the veterinary profession.” The current situation in our veterinary colleges of nutritional education coming predominantly from the pet food companies rather than from independent specialist nutritionists is like relying upon the tobacco companies for safety reports on their products. The current situation of the Canadian Veterinary Medical Association receiving a large amount of money from a veterinary pet food company is a Faustian deal at best. As a self-governing profession that primarily sells information, we must be and must be seen to be informed, independent, ethical, and caring primarily for the patient and the client. Is it not time for our veterinary colleges and our veterinary associations to extricate themselves from their, and our, conflict of interest with pet food companies?
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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 | 0.012 | 0.045 |
| Meta-epidemiology (narrow) | 0.001 | 0.002 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.010 | 0.010 |
| Scholarly communication | 0.007 | 0.012 |
| Open science | 0.008 | 0.004 |
| Research integrity | 0.096 | 0.099 |
| Insufficient payload (model declined to judge) | 0.008 | 0.006 |
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".