Bibliographic record
Abstract
PURPOSE: The purpose of this study was to develop a method for the assessment of audiology author impact as a function of institution and compare these results to a recent college ranking of audiology graduate programs. METHOD: Scopus author impact metrics (i.e., number of documents, number of citations, and h index) from a previous study (Stuart, Faucette, & Thomas, 2017) were generated for 79 accredited graduate programs in audiology in the United States and Canada. Author impact metrics were summed to represent the total institution output, and median values were calculated to reflect a measure of central tendency of individual faculty performance. RESULTS: Three hundred and seventy-nine audiology faculty members were identified and of those 86.0% (n = 326) were found in Scopus. Database presence increased with increasing rank (p = .003). Scopus index values were positively skewed. The total summed number of documents, citations, and h indices were positively correlated with the total number of faculty in the institutions and with the summed number of coauthors (p < .001). The median number of documents, citations, and h indices were not significantly correlated with the total number of faculty in the institutions but were positively correlated with the median number of coauthors (p < .001). In general, indices were higher for research/doctoral versus nonresearch universities. Higher college program rankings were statistically related with better Scopus index values. CONCLUSION: These institutional metrics may be used to serve as a benchmark for institutional production, attracting students, hiring faculty, and assessing allocation of institutional funding.
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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 | 0.052 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.019 | 0.023 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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; 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".