Bibliographic record
Abstract
In search of the clinical scientistIn 1999, Bill Gillies, then President of the Royal Australian College of Ophthalmologists, wrote that 'clinical research in the field of ophthalmology is a largely neglected field in this country' 1 further exhorting his colleagues 'with the increasing complexity and effectiveness of both medical and surgical options in our field, we need to be more active in clinical research'.Since that time, this journal has metamorphosed into Clinical and Experimental Ophthalmology , encouraging a diversity of research papers published within its covers and a more international readership.Moreover, the article by Davis and Wilson in this issue shows that Australia has significantly increased its contribution to the published ophthalmic research over the last 20 years, 2 seemingly predicting a bright future for vision science in Australasia.Does Australasia have the momentum to sustain the advances made in the last two decades?Insights into the attitude to research of consultant ophthalmologists and trainee ophthalmologists in New Zealand can be gleaned from the article by Jayasundera, Fisk and McGhee in this issue.3 At face value the article appears to support the robust future of clinical research in ophthalmology in New Zealand, with 93% of the respondents having already undertaken some form of research and a large majority (67%) intending to undertake further research in the future.However, some disturbing traits lie dormant within the results of the article which I have the opportunity to discuss here.
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 | 0.037 | 0.152 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.001 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.010 | 0.023 |
| Scholarly communication | 0.038 | 0.032 |
| Open science | 0.005 | 0.022 |
| Research integrity | 0.026 | 0.058 |
| Insufficient payload (model declined to judge) | 0.046 | 0.045 |
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".