Bibliographic record
Abstract
I consider it both an honour and privilege to write the foreword to this insightful book, Leave No Doubt, detailing the preparation and experiences that culminated in a gold medal for Canada in ice hockey at the 2010 Winter Olympic Games in Vancouver.Having known Mike Babcock for over ten years, I am truly amazed at the details and thought processes Mike chronicles in this book.Needless to say, I came to respect him for his intensity and his thoroughness.He takes the reader through his early days as a coach, with all the trials and tribulations he encountered.Mike would go on to become the only man to win a World Junior Hockey Championship, a World Hockey Championship, a Stanley Cup Championship, and an Olympic Gold Medal.Mike and I share many of the same beliefs, not just about running a successful team, but also about enjoying strong family values.He firmly believes in giving back and so he volunteers his time to a number of causes.He is very loyal to his home province of Saskatchewan, and I was proud to be in Saskatoon in July 2010 when the city held an official Mike Babcock Day.About $250,000 was raised for the new children's hospital.Mike came up through the ranks, like I did
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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 | 0.000 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.006 | 0.004 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.480 | 0.436 |
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".