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.
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 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 | 0.212 | 0.129 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.060 | 0.042 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.009 | 0.002 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.000 | 0.002 |
| 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; both teacher heads 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".