Bibliographic record
Abstract
J C P H – Vol. 60, no 3 – juin 2007 216 the time and mental energy to devote to nurturing another person. Next, an effective mentor is a person who has benefited from positive life experiences and who thus has the inherent knowledge, insight, and network so vital to the development of a protege. A final requirement is the ability to use a wide range of coaching, counselling, modelling, and facilitation skills to expose the protege to new ideas and applications, and to expand the protege’s role from dependent learner to career self-reliance. A powerful tool is the sharing of life stories (including open discussion of failures as well as successes), a process that provides valuable opportunities for analysis of life’s realities. In my own life and career, I have been the lucky recipient of the gift of mentorship from many wise individuals. But this is a gift that is only fully realized through sharing with others. Similar to Plato, who saw the importance of transferring the benefits he gained from Socrates to his own protege Aristotle, I now have the honour of passing on my humble insights to some of the students, peers, and staff pharmacists with whom I interact. Imagine my pleasure in finding that I am learning as much from these individuals as they are learning from me. In fact, the gift has truly come full circle.
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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 | 0.003 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.007 | 0.003 |
| Scholarly communication | 0.010 | 0.007 |
| Open science | 0.001 | 0.009 |
| Research integrity | 0.007 | 0.007 |
| Insufficient payload (model declined to judge) | 0.349 | 0.220 |
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".