Bibliographic record
Abstract
Was it over another bottomless glass of sangria or during the boat ride on a sparkling lake? I’m not sure exactly when it occurred to me, but I am suddenly very popular. People laugh more at my jokes, request that I be seated first, and listen to even the longest of my stories. And while I would like to believe that my Semitic features or past travels have invoked such interest, the truth is this: my name is Eric, and I am a graduating family physician. From Vancouver to Val-d’Or, Canadians are in dire need of family doctors. Recent estimates peg the shortage at 3000, and between 4 and 5 million Canadians still do not have a family physician. Given the substantial health benefits that family medicine provides to individuals, families, and communities, demand for new omnipraticiens is very high. So, communities want to know how to attract our licence numbers and our magical ability to shorten waiting lists, transform walk-in vagabonds to well-covered roster-dwellers, and perenially apparate at 2:00 in the morning when a woman delivers her baby. Thus, the seduction continues. Certainly, all of this attention is quite flattering. After living the impecunious existence of medical-school suppliant, medical-student scut monkey, and resident workhorse, this spotlight of seemingly unconditional love can feel very warm. Unfortunately, such indulgences can also impair judgment and imbue a harmful hubris in place of vigour for lifelong learning. Thankfully, a long night on call or a difficult patient encounter are often effective antidotes, reminding me that I am still very much a family doctor in the making—there is much more personal and professional growth ahead. And, as I politely decline another invitation for afternoon golf, I hope that my suitors understand this as well.
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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 | 0.002 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.011 | 0.005 |
| Scholarly communication | 0.005 | 0.006 |
| Open science | 0.001 | 0.007 |
| Research integrity | 0.004 | 0.014 |
| Insufficient payload (model declined to judge) | 0.064 | 0.043 |
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".