Bibliographic record
Abstract
Dear Editor, I feel compelled to respond to Bernard Rollins’ comments (Can Vet J 2013;54:630–631) in his reply to the April 2013 ethical question of the month regarding the use of undercover camera, and more specifically his comment encouraging video footage of slaughter houses to be available to consumers. Although I agree that people should know where their food comes from, to reveal the graphic nature of an abattoir has the potential to cause a drastic repercussion in an already floundering beef industry. Revealing slaughter procedure would have had a significantly less “shock and awe” effect on people 50–100 years ago, when a lot more people were from rural areas and were well aware of where their food came from. Cinematography has the ability to be educational rather than horrifying. Exposing urbanites to scenes on film meant to reveal the horror of an abattoir could be likened to teaching a population that has long forgotten how to swim by taking them out to the middle of a lake and pushing them in without so much as letting them get their feet wet beforehand.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 teacher head, 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".