Examining the Association Between Name Characteristics and Academic Career Success of UK Neurologists
Bibliographic record
Abstract
This study aimed to examine whether name characteristics of UK neurologists are related to their academic career success. Biographical information and bibliometrics of all UK consultant neurologists (N=1010) were obtained from online sources. Neurologists with a shorter surname and a higher consonant:vowel ratio in their surname had more citations. The surname's complexity was negatively associated with the h-index and citations, and was lower in neurologists currently affiliated with a top university. Top university graduates for their medical degree had fewer syllables in their first and last name. Neurologists with a popular forename had higher bibliometrics, were faster in publishing their first paper, more likely to be top university graduates for their medical degree and more likely to be currently affiliated with a university. Neurologists with a popular surname were more likely to be top university graduates for their medical degree. Male neurologists with more masculine forenames were more likely to be top university graduates, were faster in publishing their first paper, and had higher bibliometrics. This study revealed that there is an association between name characteristics and career success of UK consultant neurologists.
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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 | 0.002 | 0.025 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.004 | 0.007 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 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".