Bibliographic record
Abstract
Webcast sponsored by the Irving K. Barber Learning Centre. Back in the ‘80s when the Vancouver Canucks were searching for wins in a tough Smythe Division, Victor de Bonis could be found parking cars at the Pacific Coliseum. After a detour through accounting firm KPMG, de Bonis joined the Canucks operation in 1994, and has since seen various teams and ownerships come and go. Yet through perseverance, relationships and a dedication to winning, over the past two decades, de Bonis has helped turned the franchise into one of the National Hockey League’s most successful franchises. We heard how he got his start, and learn about the challenges he faced and opportunities he seized along the way. Wesbrook Talks is presented by Wesbrook Village and alumni UBC. It is designed to provide intimate opportunities to listen to and engage with prominent alumni in the community. Speaker Bio Victor de Bonis is the Chief Operating Officer for Canucks Sports and Entertainment (CSE), and an Alternate Governor for the NHL. Working in partnership with President of Hockey Operations Trevor Linden, Victor has primary responsibility over all facets of business operations and directs the Senior Leadership Team.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.002 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.326 | 0.002 |
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; both teacher heads agree on what is shown here.
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".