Bibliographic record
Abstract
I served Canada on five Olympic teams but being the Chief Medical Officer for the 2015 Pan American Games in Toronto was my most challenging games. It was the biggest project I have ever undertaken and really the only one where I felt that I was part of creating a sustainable legacy—12 Physical Activity Facilities, 2000 interprofessional Medical/Anti-doping volunteers and the first ever integrated Pan and Parapan Sport Medicine Conference…to name the top three!⇓ Julia Alleyne Making musculoskeletal medicine ‘simple’ by translating it into teachable moments and key messages. I have built many course curricula—classroom, online and blended—all interactive and many in the experiential model. Learners tell me that ‘it finally makes sense’. The true value is that our patients have better outcomes! Flexibility! That is, flexible thinking, negotiations, time management and work–life balance. Being flexible just lets you shift gears and makes you a better team player. I had been teaching for many years already, but I went …
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.022 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.000 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.005 | 0.011 |
| Insufficient payload (model declined to judge) | 0.025 | 0.020 |
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".