Author Impact Metrics in Communication Sciences and Disorder Research
Bibliographic record
Abstract
Purpose: The purpose was to examine author-level impact metrics for faculty in the communication sciences and disorder research field across a variety of databases. Method: Author-level impact metrics were collected for faculty from 257 accredited universities in the United States and Canada. Three databases (i.e., Google Scholar, ResearchGate, and Scopus) were utilized. Results: Faculty expertise was in audiology (24.4%; n = 490) and speech-language pathology (75.6%; n = 1,520). Women comprised 68.1% of faculty, and men comprised 31.9% of faculty. The percentage of faculty in the field of communication sciences and disorders identified in each database was 10.5% (n = 212), 44.0% (n = 885), and 84.4% (n = 1,696) for Google Scholar, ResearchGate, and Scopus, respectively. In general, author-level impact metrics were positively skewed. Metric values increased significantly with increasing academic rank (p < .05), were greater for men versus women (p < .05), and were greater for those in audiology versus speech-language pathology (p < .05). There were statistically significant positive correlations between all author-level metrics (p < .01). Conclusions: These author-level metrics may serve as a benchmark for scholarly production of those in the field of communication sciences and disorders and may assist with professional identity management, tenure and promotion review, grant applications, and employment.
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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 | 0.064 | 0.286 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.079 | 0.147 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.011 | 0.007 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.001 | 0.001 |
| 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; 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".