Bibliographic record
Abstract
Canada), I myself have been struggling with this very issue for as long as I can remember. How can we make a “big” impact when we can’t even reach the drugs on the highest shelves? How can we develop as national speakers when our audience can’t see us from behind the podium? How can we increase the stature of pharmacists when we exist in a climate of downsizing? With these thoughts, a campaign slogan practically suggested itself: Don’t be shortsighted — vote for me and I’ll demonstrate that height doesn’t matter (and if it does, I can always hand out growth hormone)! I had sized up the competition and was ready with a giant campaign to become your President Elect. So imagine my surprise when I found out I had been “elected” without having a chance to put these strategies into practice. I guess I’ll have to save my tattoos and drug samples and dispense them as the need arises throughout my 3 years in office. Yet I’m sure that this article has given hospital pharmacists some insights into the capabilities of their new presidential officer. They’ll know to expect me to give it my all. They’ll know that I’ll provide a fresh perspective on current issues. And they’ll know, if nothing else, that I’ll definitely be a-Musing!
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 | 0.003 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.022 | 0.007 |
| Scholarly communication | 0.017 | 0.006 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.006 | 0.011 |
| Insufficient payload (model declined to judge) | 0.119 | 0.036 |
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".