Levels of Citation of Nonhuman Animal Studies Conducted at a Canadian Research Hospital
Bibliographic record
Abstract
The publication of scientific articles that receive few or no citations raises questions of the appropriate use of resources as well as ethics. In the case of animal research, the ethics issue extends beyond human patients to nonhuman animals, as the research subjects them to pain and, typically, to death. This study is a citation analysis of animal research conducted at Toronto's Hospital for Sick Children (HSC). Of the 594 publications (1990 to 1995) on animal research by affiliates of HSC, 29% received fewer than 10 citations in a 10-year period. We compare the research history of 13 "best" and 13 "worst" HSC scientists. Worst researchers continue to do infrequently cited research. Recommendations indicate how institutions and researchers can become more effective and accountable.
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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 | 0.038 | 0.256 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.073 | 0.081 |
| Science and technology studies | 0.008 | 0.004 |
| Scholarly communication | 0.009 | 0.002 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.015 | 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".