Departmental h-Index: Evidence for Publishing Less?
Bibliographic record
Abstract
PURPOSE: The h-index is an established method for determining an individual faculty member's impact on the scientific literature. The purpose of this study was to measure and describe over time the combined h-index of a large university medical imaging department. MATERIALS AND METHODS: All faculty members from the Department of Medical Imaging, University of Toronto, were identified from administrative records for 6 separate years between 2000-2014. Individual members' and the departmental h-index were calculated using citation data from the Scopus database. Descriptive univariate statistics were reported. Factors contributing to the change in departmental h-index over time were assessed using linear regression analysis. RESULTS: The number of faculty members increased from 117 in 2000 to 186 in 2014. The departmental h-index increased from 48 in 2000 to 142 in 2014. During this time period, the median h-index for faculty members increased from 4 (interquartile range 2-8) to 10 (interquartile range 5-19). Regression analysis revealed that for every additional staff member, the departmental h-index increased by 1.4 (standard error = 0.1, P < .01), whereas, by increasing the median h-index of members by 1 the departmental h-index increased by 15.7 (standard error = 0.6, P < .01). CONCLUSION: Our study suggests that to increase a department's h-index, it is important to foster impactful research from within the faculty ranks of the department. The h-index of academic radiology departments is a meaningful tool that allows for evaluation from within and against other academic centres.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 teacher head, 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".