Bibliographic record
Abstract
J C P H – Vol. 57, n 1 – fevrier 2004 68 Council’s commitment to living this core value was evident in their establishment of the Member Recognition Task Force. The task force will review the Society’s current methods of recognizing members and provide recommendations to Council to ensure that efficient, fair, consistent, and affordable processes are in place to recognize volunteer efforts. The enhancement of member recognition processes will allow the Society to fulfill the promise of a core value and will encourage, promote, and sustain member volunteerism well into the future. Despite CSHP’s rich history of member volunteerism, promoting and sustaining these efforts will be a challenge in the years ahead. We live in an age of competing demands. Pharmacist shortages often result in additional responsibilities and hours of work for members. They are challenged to find volunteer hours in the face of workplace demands, and those of personal interests and responsibilities. Acknowledgement of volunteer efforts with thanks for a job well done will help to meet this challenge.
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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 | 0.012 | 0.037 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.012 | 0.006 |
| Scholarly communication | 0.010 | 0.004 |
| Open science | 0.001 | 0.013 |
| Research integrity | 0.005 | 0.007 |
| Insufficient payload (model declined to judge) | 0.010 | 0.003 |
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".