Bibliographic record
Abstract
I continue to appreciate the updated look of the CPJ and the consistent high quality and applicability of the articles. The Original Research contribution1 on the legibility of prescription medication labelling in Canada came at a convenient time for me. I had recently contacted our software vendor to change the fact that the words in the instructions on our prescription labels print out as upper case. This software vendor will not charge the client for upgrades when it is shown that a national or provincial standard exists, and thus you have strengthened my case that we should not have to pay for the programming time to implement this change. The list of references in this article was extensive, current and convincing. I found the guidelines on the Macular Society of the UK website a succinct summary on writing for visually impaired patients (www.macularsociety.org/How-we-help/Eye-care-professionals/Innovation-and-good-practice/Writing-for-visually-impaired-people). I know there are also guidelines regarding accessibility of website pages for the visually impaired, and these should be considered for pharmacies maintaining a website.
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 | 0.064 | 0.487 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.010 | 0.011 |
| Science and technology studies | 0.005 | 0.007 |
| Scholarly communication | 0.016 | 0.009 |
| Open science | 0.003 | 0.005 |
| Research integrity | 0.004 | 0.007 |
| Insufficient payload (model declined to judge) | 0.018 | 0.004 |
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".