Capilano University President's service awards: Celebrating 40 years
Bibliographic record
Abstract
When I joined Capilano College 37 years ago, it was a very young college that had only been open for three years.Since those early days, I have been profoundly moved by the overwhelming support and contributions that have been offered to Capilano from numerous individuals and organizations.Each and every one has made an enormous difference to the success of the institution and to the achievements of our students.This year marks the 40th since we first opened our doors and we felt the time was opportune for us to do something special for all those who have helped us and our students over the years.On November 15 we held the 2008 President's Service Awards at our North Vancouver campus, where we recognized those citizens, groups and organizations that have helped to further Capilano University's mission of enabling student success.The names of the recipients were put forward through a public consultation process that was held several months ago.While the list is extensive, I recognize that it is by no means complete.The number of people and organizations who have helped us over the past 40 years is enormous.And so, to all those whose names are listed in this paper, and to all those whose names do not appear, please accept my deepest appreciation for your past and ongoing support.Your contributions and their importance to our students and alumni are invaluable and I remain extremely grateful.
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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 | 0.007 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.009 | 0.002 |
| Scholarly communication | 0.015 | 0.003 |
| Open science | 0.001 | 0.009 |
| Research integrity | 0.005 | 0.009 |
| Insufficient payload (model declined to judge) | 0.048 | 0.021 |
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".