Bibliographic record
Abstract
C J H P – Vol. 56, No. 5 – November 2003 J C P H – Vol. 56, n 5 – novembre 2003 closed the books on the 2002/2003 fiscal year with a $174,151 operating surplus. At the Annual General Meeting (AGM) in St. John’s, Desarae Davidson, Conference A d m i n i s t r a t o r , orchestrated a program that involved some 25 speakers, 220 registered pharmacists, and 26 exhibit booths, this in the aftermath of the worst power outage in Canadian history. Janet Lett, Executive Assistant, masterminded 8 business meetings of CSHP’s Executive, Council, and general membership, all held in conjunction with the AGM. In addition to celebrating her 10th anniversary in CSHP’s employ, Gloria Day, Administrative Assistant, shepherded the Canadian Hospital Pharmacy Residency Board through a most productive meeting. Laurie Carquez, Membership Administrator, stepped in at the last minute to assist at the AGM registration desk in St. John’s, temporarily setting aside the quickly accumulating membership renewals (over 1800 as of October 2, 2003). Katral-Nada Hassan, Journal Administrator, produced a special supplement to the Journal on behalf of the Association of Faculties of Pharmacy of Canada. This dedication, to the power of 6, made it easy for me to solve the CSHP’s head office operational equation!
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 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.007 | 0.004 |
| Scholarly communication | 0.007 | 0.003 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.004 | 0.009 |
| Insufficient payload (model declined to judge) | 0.226 | 0.057 |
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".