Bibliographic record
Abstract
OBJECTIVE: The validity of using publication statistics to evaluate university faculty is not established. This study aimed to determine if publication statistics vary among psychiatric faculty members of different academic rank and if there are biases among disciplines. METHOD: Using the 10 most recent publications written by psychiatric faculty members at 2 schools of medicine, we compared the time to publish 10 papers, the 5-year impact, the citation rate, and the citation ratio according to academic rank and school. Leaders in neuroscience were compared with leaders in clinical subspecialties. RESULTS: All statistics were associated with academic rank (P < or = 0.001) and there were significant differences between the 2 schools. There were more basic scientists than clinical subspecialists in the 80th percentile for 5-year impact (P = 0.04), but the latter disciplines performed equally in citation ratio. CONCLUSIONS: Publication statistics differ among academic ranks. Citation ratio minimizes the effect of biases among disciplines. Publication statistics may provide useful information for evaluating psychiatric faculty.
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 | 0.403 | 0.825 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.006 | 0.006 |
| Bibliometrics | 0.053 | 0.077 |
| Science and technology studies | 0.002 | 0.011 |
| Scholarly communication | 0.010 | 0.012 |
| Open science | 0.004 | 0.006 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".