Bibliographic record
Abstract
This paper looks at optometry museums around the world. There are only five general optometry museums: three are hosted by optometric institutions in three countries, Australia, Britain and the U.S.A., one is hosted by a Canadian university that has an optometry school, and one is in private hands in Southbridge, Massachusetts. They are supplemented by six excellent corporate museums in France, Germany and Italy, but these museums focus on either spectacles or ophthalmic instruments, rather than optometry in general. Two of the optometry museums were founded over 100 years ago, and two have had their 50th birthday, but can they survive forever? Museums are expected to preserve collections for posterity for the edification and enjoyment of future generations, yet all institutions are at risk of disruption: few institutions last more than a couple of hundred years. This paper discusses strategies optometry museums might pursue to guard against mismanagement and neglect and provide for the protection of their collections in the event of the demise of the museum or its host institution.
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 | 0.008 | 0.015 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.017 | 0.031 |
| Scholarly communication | 0.013 | 0.012 |
| Open science | 0.001 | 0.010 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.012 | 0.003 |
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".