Bibliographic record
Abstract
JCPH – Vol. 66, n 4 – juillet–aout 2013 272 Throughout my career, the Canadian Society of Hospital Pharmacists (CSHP) has been a constant companion. I got involved with CSHP upon completing my residency and participated actively at the Branch level, holding several positions on Council. Over the past few years, I have been fortunate to become more involved at the National level, serving as the CSHP representative on the Canadian Council on Continuing Education in Pharmacy and, more recently, as the British Columbia Branch delegate to National Council. The dedicated volunteers who put in tireless hours to make CSHP and the profession better certainly exemplify the sentiments expressed in the Margaret Mead quote above. So where does this long and winding road that I have travelled now lead? I look forward to my term on the CSHP Executive and especially to working with dedicated staff in the CSHP office and the talented individuals at the branch level. I will finish with another quote from Dr Seuss: “You’re off to Great Places! Today is your day! Your mountain is waiting. So ... get on your way!”
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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 | 0.002 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.010 | 0.004 |
| Scholarly communication | 0.005 | 0.005 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.004 | 0.010 |
| Insufficient payload (model declined to judge) | 0.105 | 0.049 |
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".