Bibliographic record
Abstract
JCPH – Vol. 64, n 3 – mai–juin 2011 232 my crawling son somehow got the remote control off the couch by pulling a pillow toward him, I knew my world would never be the same . . . outsmarted by a 9-month-old! Change can come quickly, and sometimes we just need to roll with it. Although we cannot anticipate everything, we can and must quickly adapt to the new opportunities and challenges that present themselves. We need to build relationships, grow, learn, develop, adapt, share, take some small steps, fall, take some big leaps, embrace the little things, and remember to have fun. When I look into my son’s eyes, I dream of a world of opportunities for him. Today, I also see opportunities for our profession that are within our grasp. We can make a difference, one Society, one provincial Branch, one pharmacy department, and one pharmacist at a time. I can only imagine where we will be in another 10 years. It has been an absolute pleasure serving as a CSHP presidential officer for the past 3 years. I will forever cherish the memories and the new friendships that I’ve gained through this position, just as I treasure the experience of becoming a new dad. Cheers!
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 | 0.014 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.005 | 0.003 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.003 | 0.007 |
| Insufficient payload (model declined to judge) | 0.032 | 0.007 |
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".