Bibliographic record
Abstract
I have to say that I think your recent editorial1 about self-denigrating words in pharmacy was bang on! What great words and a fantastic way to train us into respectful linguistics. My biggest beef right now is the auxiliary pharmacy world (a.k.a., drug reps, health liaisons, manufacturers, industry leaders, etc.) still using the term dispensing fee in articles, discussions and forums. Another phrase I have trouble with is cognitive services—this to me implies that when we are preparing a prescription or reviewing drug appropriateness and drug/food/alternative therapy interactions, we are not using cognition? I have not researched the current stats on drug-related problems (DRPs) and emergency room (ER) visits, but previously, most of the DRPs that resulted in ER visits could and should have been avoided with drug experts on task. Perhaps some pharmacists are on auto-pilot during Rx-related activities?
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 | 0.019 | 0.105 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.005 | 0.013 |
| Scholarly communication | 0.009 | 0.016 |
| Open science | 0.003 | 0.008 |
| Research integrity | 0.003 | 0.011 |
| Insufficient payload (model declined to judge) | 0.014 | 0.011 |
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".