Bibliographic record
Abstract
As governor generAl of Canada, one of my great privileges and responsibilities is to help celebrate our country and the people who call it home.This year, which marks the 150th anniversary of Confederation, is one of special celebration.However, it's important that, even as we celebrate and honour our remarkably diverse and successful country, we avoid the trap of complacency and self-satisfaction.After all, we can and must do better on so many important issues.My mandate as governor general has focused on encouraging Canadians to help build a smarter, more caring nation, a broad focus which recognizes that, fortunate as we are to live in this country, there remain many areas for improvement and that require our sustained and thoughtful attention.This collection of essays, which gathers contributions from some of Canada's brightest minds on a wide variety of social, environmental, economic, political, scientific and cultural topics, marks an important contribution to our sesquicentennial celebrations.In recent years I have often asked Canadians to consider what their gifts to Canada will be on the occasion of its 150th birthday.This volume offers us a timely and relevant gift of thoughts and ideas on some of the most pressing issues of our time.There is a great wealth of insight and analysis within these pages that gives us all much food for thought as we reflect upon Canada in this milestone year and look towards our shared future.I thank the contributors and everyone who played a role in bringing this volume to fruition.
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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 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.006 | 0.001 |
| Scholarly communication | 0.007 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.553 | 0.448 |
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".