Bibliographic record
Abstract
IN THE 1990Swas a thirty-year-old freshly minted family doctor working in a rural village of 800 people.I'd left my family and friends and colleagues in Sydney for the wide horizons of a life as a country doc.It was a spur of the moment decision.I'd stopped for a toilet break on a road trip.Got chatting with the woman in the ice cream shop.She told me that she had moved from the city and how much she enjoyed her new life."Sounds great.You don't happen to know of a job for a doctor in town, do you?"I quipped."Well, as a matter of fact, one of our doctors was killed in a car accident last week," was her response which changed the direction of my life.Within a couple of months, I was the new doctor in town.I was different to my predecessor.I was younger.Less experienced.Male.The people I now cared for tried to help me adjust to my new environment."Dr Carol didn't do it that way!" So, I learnt to pretend.I worked very hard at trying to be a good doctor.It was exhausting.Sometimes the veneer of bravado was sufficient.Thankfully I didn't have to pretend when I saw the children of the village.They were happy to accept me as I was.They didn't mind that I wasn't Dr Carol.They only cared that I cared.Once their parents could see that their children were happy to come to see me, they relaxed a bit too.Word must have spread around town that the new doctor was okay."My friend (or daughter or wife) said I should make an appointment to see you."I
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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 | 0.007 | 0.030 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.012 | 0.030 |
| Scholarly communication | 0.013 | 0.016 |
| Open science | 0.001 | 0.013 |
| Research integrity | 0.004 | 0.022 |
| Insufficient payload (model declined to judge) | 0.011 | 0.007 |
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".