An updated h-index measures both the primary and total scientific output of a researcher
Bibliographic record
Abstract
The growing interest in scientometry stems from ethical concerns related to the proper evaluation of scientific contributions of an author working in a hard science. In the absence of a consensus, institutions may use arbitrary methods for evaluating scientists for employment and promotion. There are several indices in use that attempt to establish the most appropriate and suggestive position of any scientist in the field he/she works in. A scientist's Hirsch-index (h-index) quantifies their total effective published output, but h-index summarizes the total value of their published work without regard to their contribution to each publication. Consequently, articles where the author was a primary contributor carry the same weight as articles where the author played a minor role. Thus, we propose an updated h-index named Hirsch(p,t)-index that informs about both total scientific output and output where the author played a primary role. Our measure, h(p,t) = h(p),h(t), is composed of the h-index h(t) and the h-index calculated for articles where the author was a key contributor; i.e. first/shared first or senior or corresponding author. Thus, a h(p,t) = 5,10 would mean that the author has 5 articles as first, shared first, senior or corresponding author with at least 5 citations each, and 10 total articles with at least 10 citations each. This index can be applied in biomedical disciplines and in all areas where the first and last position on an article are the most important. Although other indexes, such as r- and w-indexes, were proposed for measuring the authors output based on the position of researchers within the published articles, our simpler strategy uses the already established algorithms for h-index calculation and may be more practical to implement.
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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 | 0.016 | 0.057 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.022 | 0.018 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.005 | 0.007 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 0.002 |
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".