'If I can finish it, I will': the inside story of Terry Fox's run
Bibliographic record
Abstract
Guest: Bill Vigars, author of \"Terry & Me\"It was 43 years ago when Terry Fox dipped his leg in the Atlantic Ocean and embarked on a run across the country to raise money for cancer research. He ran 5,373 kilometres in 143 days before his cancer would return and end his run in Thunder Bay, Ont. Fox's dream of raising $1 for every Canadian would be realized, though. Canadians and others around the world run annually in his place and have raised over $850 million dollars for critical research. Bill Vigars, one of the people closest to Fox, is the author of the new book \"Terry & Me\" and joins \"This Matters\" to share more about the man behind the Marathon of Hope.This episode was produced by Saba Eitizaz, Paolo Marques and Brian Bradley.Audio Sources: Terry Fox Foundation, CHCH News, Global News
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.010 | 0.002 |
| Scholarly communication | 0.007 | 0.004 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.003 | 0.006 |
| Insufficient payload (model declined to judge) | 0.146 | 0.064 |
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".