Utilizing the Hirsch index to compare top obstetrics and gynecology researchers and the effect of readership volume on establishing solid benchmarks
Bibliographic record
Abstract
Background Bibliometric indices that are used to compare researchers should precisely assess their eligibility to lead or become members of relevant academias and organizations. The Hirsch (h) index is the most universally used index. Previous studies have established the use of this parameter to set benchmarks in physics and biology. Aim The aim of the study was to assess the h-index’s ability to provide benchmarks in the evaluation of researchers for leading reference Societies, Colleges, or Academias in Obstetrics and Gynecology. Materials and methods The h-indices of International Federation of Gynecology and Obstetrics (FIGO) presidents, American College of Obstetrics and Gynecology (ACOG) presidents, Nobel Prize holders in medicine and physics, and chief editors of Obstetrics and Gynecology journals were compared. Readership volume of journals on physics and medicine was assessed and compared with each other. Results There were no clear benchmarks for individually assessed h-indices. Wide interquartile ranges were seen. A wide range in the number of citations and papers was seen in the case of Nobel Prize holders of different disciplines. Conclusion Considering readership volume alone makes the h-index unsuitable for setting a clear benchmark for predicting eligibility for leading positions in research and practice organizations. Further studies to establish a field-dependent method are recommended. The new method should be sensitive to the particular branch’s policymaking practices, to the researcher’s behavior and attitude, and to his or her contributions to the field of maternal and fetal welfare, both at research and clinical levels.
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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 | 0.075 | 0.301 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.025 | 0.022 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.007 | 0.007 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".