On “Journal publication productivity…” Richter et al. Phys Ther. 2008;88:376–386.
Bibliographic record
Abstract
Having published an article on pediatric physical therapy publication trends in 1993,1 I was keenly interested in reading Richter and colleagues’ recent article on publication productivity in US academic physical therapy programs2 and the letters written in response to it.3–5 Kudos to Randy Richter and his colleagues for conducting this important study, despite some methodological shortcomings that were pointed out by the authors themselves2 and the respondents.3–5 I agree wholeheartedly with Christopher Maher4 that this study should not be ignored! Having been an academic in physical therapy programs in 2 major research-intensive universities in the United States from 1981 to 1989, I moved to Canada and the University of British Columbia's (UBC) physical therapy program in 1990 and have remained there ever since. Not only are there far fewer physical therapist education programs in Canada (n=13) than in the United States, they also are much more standardized in their approach. All 13 are located in research-intensive universities and are part of major health sciences centers.
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 | 0.010 | 0.104 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.004 | 0.004 |
| Scholarly communication | 0.007 | 0.005 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.032 | 0.024 |
| Insufficient payload (model declined to judge) | 0.013 | 0.016 |
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".