Bibliographic record
Abstract
John Senders is Professor Emeritus of Engineering at the University of Toronto and Lecturer in Law at York University in Toronto. He has spoken and written on the nature and source of human error since 1976. He is Principal Scientific Consultant to the Institute for Safe Medication Practices (ISMP) and a member of the board of its Canadian offspring (ISMP-Canada). After a routine visit to his physician he unexpectedly found himself on the cardiac treadmill and 10 days later had a quadruple coronary bypass at a major Toronto hospital with an excellent reputation in coronary surgery. He recovered rapidly and is back at work . For the first time in many years I entered a hospital not to ask questions and to make recommendations but to undergo a quadruple coronary bypass. Aside from the time spent under anesthesia, I found myself for the next two weeks asking questions and making recommendations. There had been some dark patches. By the time I had been moved into the intensive care unit I was in a deep depression, with all the attendant distortions of perception and affect. I had not been warned about this, nor was any help given in dealing with it. I was hearing voices, seeing sounds, and completely disoriented. It felt like a bad trip. A fellow patient in an adjoining room was even worse off; he spoke only of suicide. Yet the hospital did nothing for him. He refused to stay in the hospital and it was left to me to talk with him and hold him together until his family arrived to take him home. To my surprise I learned that all the staff were …
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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 | 0.003 | 0.016 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.009 | 0.007 |
| Scholarly communication | 0.007 | 0.008 |
| Open science | 0.002 | 0.008 |
| Research integrity | 0.008 | 0.027 |
| Insufficient payload (model declined to judge) | 0.026 | 0.010 |
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".