INTERVIEW/COMMENTARY Ross Graham (RG): What attracted you to public health?
Bibliographic record
Abstract
I was tired of patching people up and sending them back into the war. I wanted to go upstream. This interest was also triggered by my involvement in ecological politics and my work at a health centre in Etobicoke where the majority of patients were on some form of social support. Their problems were clearly not medical problems – they were social and economic problems. As well, in 1996 I returned to a small town in northern Borneo where I had worked as a volunteer 30 years prior. During that trip, I realized that my interests in healthy communities and the ‘beyond health care ’ movement were likely founded during that volunteer experience in the 1960s, before I went to medical school. I realized after I came back that their community was in many ways very happy and healthy. They weren’t dying of starvation, and were generally healthy and productive. Yet, they had very little of the so-called ‘benefits ’ of western medicine.
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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 | 0.008 | 0.039 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.012 | 0.007 |
| Scholarly communication | 0.005 | 0.007 |
| Open science | 0.003 | 0.005 |
| Research integrity | 0.047 | 0.059 |
| Insufficient payload (model declined to judge) | 0.012 | 0.004 |
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".