The History of Optometry Journals from a Bibliometric Perspective
Bibliographic record
Abstract
The rich history of optometric journal publications has been well documented, but the scientific impact of all optometry journals over all time has not been published. This work aims to determine the most impactful papers, authors, institutions and countries publishing in optometry journals. A h-index for “optometry journal publications” (the “hOJP-index”) was derived for each constituent of each category to serve as a measure of impact. The hOJP-index for the 34,565 papers published in all optometry journals is 136; these papers have been cited 294,239 times. Optometry and Vision Science is the most impactful and prolific journal (hOJP=118; n=13,095 papers). The most highly cited paper, by Richard Armstrong, is entitled “When to use the Bonferroni correction” (1,172 citations). Australian optometrist Nathan Efron is the most impactful and prolific author (hOJP=41; n=273). UNSW Sydney and the University of California, Berkeley are the most impactful institutions (both hOJP=58), and UNSW Sydney is the most prolific (n=963). The most impactful and prolific nation is the United States (hOJP=109; n=12,050). This quantitative bibliometric analysis demonstrates an impactful optometric research base enshrined in optometry journals.
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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 | 0.008 | 0.047 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.081 | 0.172 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.010 | 0.008 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.009 | 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".