Who cites optometry journals?
Bibliographic record
Abstract
This work seeks to identify the most impactful journals, papers, authors, institutions, and countries that cite optometry journal articles. The Scopus database was searched for papers citing at least one article published in any of the 18 optometry journals included in that database (i.e. ‘optometry articles’). The 10 most highly cited papers that cite optometry journal articles were determined from 82,830 papers found. A h-index for “optometry journal citations” (the hOJC-index) was derived for each entity in the categories of journals, papers, authors, institutions and countries to serve as a measure of impact. The hOJC-index of the body of papers citing optometry journal articles is 370. Papers citing optometry journal articles have themselves been cited 2,054,816 times. Investigative Ophthalmology & Visual Science (hOJC = 154) is the most impactful journal citing optometry articles and Optometry and Vision Science the most prolific (5310 papers). The most impactful paper citing optometry journal articles (5725 citations) was published in Journal of Clinical Epidemiology. Ophthalmologist Seang Mei Saw (hOJC = 69) is the most impactful author and optometrist Nathan Efron is the most prolific (288 papers). Harvard University (hOJC = 127) is the most impactful and UNSW Sydney is the most prolific institution (1761 papers). The United States is the most impactful and prolific nation (hOJC = 313; 28,485 papers). Optometry journal articles are cited extensively by optometrists, ophthalmologists, and vision scientists world-wide, as well as authors from a broad spectrum of non-ophthalmic research domains. This work confirms the utility and influence of optometry journals.
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 | 0.012 | 0.107 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.068 | 0.074 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.014 | 0.011 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.003 | 0.001 |
| Insufficient payload (model declined to judge) | 0.018 | 0.008 |
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".