Bibliographic record
Abstract
Once upon a time, there was a pre-pharmacy student who wrote a “motivation letter”. As was the case for all applicants to the University of Kentucky College of Pharmacy, she had to convince the selection committee (in 500 words) that her motivation for becoming a pharmacist qualified her for acceptance to pharmacy school. That student was me, and I vividly recall how I struggled to write that letter. I ended up listing such lofty goals as “helping mankind by making a significant difference in the well-being of patients” and “having a fulfilling professional career”. Now, almost 32 years later, I find myself writing another motivation letter. As I embark on my 5-year term as Editor of CJHP, I ask myself what exactly has motivated me to take on this major endeavour. This time, the answer is easy and requires just 16 words: “I would like to give something back to the profession that has given me so much.” Little did I appreciate that those big words in my pharmacy motivation letter would ring so true and be so directly applicable today in my role as Editor. Back then, I used the words more as a way to gain acceptance to pharmacy school than to describe my true feelings. Now, I can’t think of words any more appropriate to describe exactly how I feel.
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 | 0.005 | 0.039 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.005 | 0.001 |
| Scholarly communication | 0.005 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.012 | 0.008 |
| Insufficient payload (model declined to judge) | 0.162 | 0.073 |
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".