Bibliographic record
Abstract
By the time you read this editorial, we will have been in the grips of the pandemic for nearly 18 months with no end in sight.Nevertheless, life goes on, and some semblance of pre-COVID activity is returning to many parts of the country despite a fourth wave among the unvaccinated.How we see patients in our practices has changed profoundly and it may be years before the safety precautions are lifted.But our patients' needs for eye care have not changed, and they rely on us to deliver that care.In Ontario, a long-festering disagreement with the province over fees for insured services is about to put a significant segment of the population in the crossfire between optometrists and the Ministry of Health.Having spent my entire professional life in Ontario, it is impossible for me to be indifferent about this unfortunate situation.Equally, as a member of the affected patient population, it concerns me greatly that my practicing colleagues do not appear to have the respect of the bureaucrats who are supposed to be negotiating with them.As health professionals, we are taught to keep personal feelings out of our professional lives, but this intolerable situation is intensely personal.At the time of writing, I don't know whether a proposed arbitration process will be accepted, let alone successful, and how this matter is concluded is sure to have an impact across Canada.Let us hope that a mutually satisfactory solution to the impasse can be found so that optometrists can continue to provide optimal vision care to their patients.
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 | 0.002 | 0.013 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.008 | 0.003 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.008 | 0.010 |
| Insufficient payload (model declined to judge) | 0.150 | 0.128 |
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".