Research Performance Measures and the Moderating Role of Faculty Characteristics in Epidemiology
Bibliographic record
Abstract
Several numeric measures have been proposed to evaluate the individual researchers' scientific performance. Among these measures, h-index is the most common and well recognized measure of research productivity and impact in scientific communities. However, empirical investigations and recent inspections revealed some shortcomings and limitations of this measure. In order to complement these limitations, several variants have been proposed in which g-index and ar-index were among the most discussed measures. The aim of this study was to examine h-index, g-index and ar-index across Iranian epidemiologists to identify the moderating characteristics as well as the distribution of these measures in the field. Using Web of Science Database, a list of Iranian epidemiologists was searched and total number of articles, total citations, and citations per paper, h-index, scientific age, g-index and ar-index were extracted and calculated for any epidemiologist. Descriptive statistics and multivariate linear regression models were used to examine research performance measures of Iranian epidemiologists. According to our findings, research performance measures found to be statistically associated with scientific age and academic ranking of Iranian Epidemiologists. Gender differences were not relevant to research performance across different measures.
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 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 | 0.035 | 0.130 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.012 | 0.021 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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 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".