24 issues per year, 25 member societies, 1.5 million podcast listens and 6.5 million YouTube views
Bibliographic record
Abstract
When the current editorial team was given the privilege and responsibility of leading the BJSM into the 2010s, part of the job description was to write a ‘Warm Up’ for each of the 12 annual issues. We have dodged that responsibility pretty consistently and still get paid. However, at the end of our 10th year in the role we share 10 Level 5 opinions. 1. BJSM aims to serve the clinical community. This resulted from our ‘listening for direct’ consultations in 2008. It meant that BJSM differentiated itself from leading physiological journals such as Journal of Applied Physiology and also from surgical journals such as the American Journal of Sports Medicine . BJSM was for clinicians who treat those in the physical activity, exercise and sporting community and those who use physical activity, exercise and sport as medicine. 2. 25 member societies make up the current ‘ BJSM family’ without including ‘ the special’ relationships we have with organisations such as the International Olympic Committee, the World Confederation of Physical Therapy, the Concussion in Sport Group (CISG), the International Federation of Sports Physical Therapy etc. The value proposition is that by being a full BJSM member, a …
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 | 0.006 | 0.030 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.007 | 0.005 |
| Science and technology studies | 0.005 | 0.002 |
| Scholarly communication | 0.013 | 0.005 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.282 | 0.144 |
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".