Bibliographic record
Abstract
I hAve the privilege of being the fifteenth president of UBC as the university enters its next century.My move to UBC was, in a sense, a homecoming: I was born in Vancouver while my father was a UBC professor of mathematics.But the move was also a positive choice to join an institution known around the world as a leader in research, education, and social engagement.Perched on a peninsula jutting into the Salish Sea, UBC's Point Grey campus sits in a breathtakingly beautiful setting, with views up Howe Sound, to the downtown high-rises of Vancouver and North Shore Mountains, and across the sea to the Gulf Islands.This site is the traditional, ancestral, and unceded lands of the Musqueam people, members of the Coast Salish Nation.This fact reminds us daily that the future of Canada must be one in which we engage honourably with Indigenous peoples who have lived on this land since time immemorial.Our university and others are called to play a critical role in shaping a just, equitable, and prosperous society, and we must engage deeply in scholarship and commentary that touches on issues of reconciliation and other pressing concerns facing Canada on the 150th anniversary of Confederation, and beyond.This book brings together some of Canada's foremost thinkers to contemplate the future of this country, with all its multi-faceted challenges
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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 | 0.001 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.006 | 0.001 |
| Scholarly communication | 0.006 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.004 |
| Insufficient payload (model declined to judge) | 0.379 | 0.234 |
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".