Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
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 | 0.003 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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 it