Bibliographic record
Abstract
Abstract It was argued recently that the g‐index is a measure of a researcher's specific impact (i.e., impact per paper) as much as it is a measure of overall impact. While this is true for the productive “core” of publications, it can be argued that the g‐index does not differ from the square root of the total number of citations in a bibliometrically meaningful way when the entire publication list is considered. The R‐index also has a tendency to follow total impact, leaving only the A‐index as a true measure of specific impact. The main difference between the g‐index and the h‐index is that the former penalizes consistency of impact whereas the latter rewards such consistency. It is concluded that the h‐index is a better bibliometric tool than is the g‐index, and that the square root of the total number of citations is a convenient measure of a researcher's overall impact.
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 | 0.020 | 0.152 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.003 | 0.001 |
| Bibliometrics | 0.015 | 0.024 |
| Science and technology studies | 0.001 | 0.005 |
| Scholarly communication | 0.009 | 0.010 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 0.004 |
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".