Bibliographic record
Abstract
research and education, this event provided an opportunity to commend Peter Loewen and Brian Tuttle on their new status as Fellows and to give $22,500 worth of grants toward 4 research projects. In February, CSHP met again twice over the issue of medicinal marihuana, first with other pharmacy organizations and then with a variety of stakeholders. During these sessions, Health Canada solicited feedback on further proposed amendments to the Marihuana Medical Access Regulations, targeted for completion by summer 2004, and provided the opportunity for an exchange of ideas and concerns among stakeholders. A pilot project involving pharmacy-based distribution is among the issues under consideration. (For additional information, see http://www.hc-sc.gc.ca/hecs-sesc/ocma.) One more external liaison event was held in February. The Canadian Foundation for Pharmacy invited the executive staff of Canadian pharmacy associations and the deans of the Canadian faculties and schools of pharmacy to participate in a workshop on how to design effective innovation. CSHP staff members can’t wait to unveil the Society’s new Web site. It is shaping up very nicely: a more functional architecture and a modern, visually attractive concept! We hope you will like it just as much as you liked the PPC.
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 | 0.002 | 0.016 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.008 | 0.002 |
| Scholarly communication | 0.009 | 0.005 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.006 | 0.009 |
| Insufficient payload (model declined to judge) | 0.301 | 0.208 |
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".