Journalism for the Public Good: The Michener Awards at Fifty
Bibliographic record
Abstract
Journalism makes a difference. In-depth investigation and reporting can break preconceptions, expose hidden truths, and have deep impacts on both public perception and public policy. Evidence-based journalism is essential in a world where information is free, and facts are disputed. The Michener Awards, named after Governor General Roland Michener, for half a century have recognized the important role of free media within democracy and have honoured the organizations that invest in public interest journalism. Journalism for the Public Good is the story of the Micheners as told through the award-winning reporting they have celebrated since the 1970s. This book feature outstanding examples of hard-hitting investigative journalism that have made an impact on the lives of Canadians. It documents the successes and struggles of the Michener Awards and the its volunteers. It traces how journalism has evolved, influenced, and been changed by Canadian society over the past half-century, and it explores the challenges journalists working in a multi-platform world face today. Journalism for the Public Good is a celebration of the organizations and individuals who give voice to marginalized communities, challenge the powerful, and through their fearless journalism make Canada a better place.
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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 | 0.004 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.018 | 0.006 |
| Scholarly communication | 0.016 | 0.006 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.004 | 0.004 |
| Insufficient payload (model declined to judge) | 0.017 | 0.004 |
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".