Using Google Scholar to track the scholarly output of research groups
Bibliographic record
Abstract
INTRODUCTION: It is often necessary to demonstrate the impact of a research program over time both within and beyond institutions. However, it is difficult to accurately track the publications of research groups over time without significant effort. A simple, scalable, and economical way to track publications from research groups and their metrics would address this challenge. METHODS: Google Scholar automatically tracks the scholarly output and citation counts of individual researchers. We created Google Scholar profiles to track the scholarly productivity of five research groups: an institutional educational research program, a division of emergency medicine, a department of emergency medicine, a national educational scholarship working group, and an international organization dedicated to online education. We added the publications of each group member to their respective group Google Scholar profile and a junior faculty member monitored the citations that were suggested. RESULTS: Google Scholar tracked a diverse collection of five research groups over 6-36 months. In addition to having different organizational structures and purposes, the groups varied in size, consisting of 8-60 researchers, and prolificacy, with group citation counts between 1006-58,380 and group h‑indexes ranging from 19-101. DISCUSSION: We anticipate that as this innovation becomes better known it will increasingly be adopted by traditional and non-traditional research groups to easily track their productivity and impact. Additional initiatives will be needed to standardize reporting guidelines within and between institutions.
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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 | 0.010 | 0.225 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.002 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".