Bibliographic record
Abstract
> The illiterate of the 21st century will not be those who cannot read and write, but those who cannot learn, unlearn, and relearn. — Alvin Toffler1 In the spring of 2018, a round-Tan ALTan ALtable discussion took place in the presence of 23 rheumatology trainees from the province of Ontario, Canada. The topic of the discussion was lifelong learning. On the panel were 5 rheumatologists representing different stages of career experience: Dr. A, a recently graduated trainee who is currently in community practice; Dr. B, an experienced community rheumatologist in a small city of Ontario; Dr. C, a senior investigator at the University of Toronto; Dr. D, a mid-career academic rheumatologist/epidemiologist; and Dr. E, a senior community rheumatologist. The panel was asked to open with statements describing their efforts and strategies to remain abreast of their professional knowledge. Dr. A noted that because of the volume of knowledge she had studied for recent rheumatology certification examinations, she did not expect that knowledge maintenance was something she would need to be concerned about for some time. However, once in practice, it became apparent that she would need to consider her own continuing education needs because she was no longer in a university setting where she attended mandatory weekly lectures. Dr. A subscribed to an online service that sent her 12 to 15 articles a month in the topic areas that she selected. She also found it useful to periodically discuss complex cases with more experienced colleagues. Dr. B, a mid-career community rheumatologist, was one of only 2 rheumatologists in a catchment area of over 350,000 people. It quickly became evident that every … Address correspondence to Dr. A.A. Bookman, Toronto Western Hospital, University Health Network, 399 Bathurst St., Room 1E424, Toronto, Ontario M5T 2S8, Canada. E-mail: arthur.bookman{at}uhn.on.ca
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 | 0.012 | 0.065 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.014 | 0.012 |
| Scholarly communication | 0.008 | 0.011 |
| Open science | 0.002 | 0.009 |
| Research integrity | 0.019 | 0.022 |
| Insufficient payload (model declined to judge) | 0.020 | 0.009 |
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".