Global optometrist research ranking derived from a science-wide author database of standardised citation indicators
Bibliographic record
Abstract
Publications in refereed scientific journals provide a rigorous research base that underpins clinical optometric practice. Leading optometrists who generate this literature can be identified and ranked using standardised citation indicators. This work seeks to identify and rank all optometrists included in a Science-Wide author database of standardised citation indicators (S-W) and to compare this ranking with the Global Optometrist Top 200 Research Ranking (T200). A search was conducted for the names of all optometrists in the T200 who were included in the S-W, which is a world-wide listing of the top 2% of scientists in each of 174 subfield disciplines, ranked according to a composite citation indicator (<i>c<sub>ns</sub></i>) that excludes self-citations and corrects for multiple authorships and author order. The names of 66 optometrists are found in the S-W. Of these, 58 are designated as working in the primary sub-field ‘Ophthalmology & Optometry’; this listing, in rank-order of <i>c<sub>ns</sub></i>, is referred to as the ‘S-W-derived Optometrist Research Ranking’ (S-WORR). Australian optometrist Nathan Efron is ranked #1 in the S-WORR. The number (%) of optometrists in the S-WORR from each country is: the United States – 26 (45%), Australia – 12 (21%), the United Kingdom – 11 (19%), Canada – 5 (9%), Spain – 2 (3%), Hong Kong – 1 (2%) and South Africa – 1 (2%). The universities housing the equal highest number of optometrists in the S-WORR (five each) are the University of California, Berkeley, USA; the University of New South Wales, Australia; and Queensland University of Technology, Australia. There is a moderately strong correlation between T200 and S-WORR rankings (ρ = 0.6017, N = 58, p The S-WORR represents an elite cohort of optometrists who ought to be celebrated for their outstanding, leading and impactful contributions to optometric research.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.004 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.346 | 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 teacher head, 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".