Review of <i>Keep True: A Life in Politics. </i>By Howard Pawley. Forewordby Paul Moist
Bibliographic record
Abstract
Howard Pawley served as premier of Manitoba from 1981 to 1988, a period that stands to this day as one of the most controversial and turbulent in the province's modern history. Keep True: A Life in Politics traces Pawley's life and political career from his birth in Brampton, Ontario, to the defeat of his government in 1988 at the hands of one of his own caucus members. Educated as a teacher and lawyer, Pawley became active in the Cooperative Commonwealth Federation (which later became the New Democratic Party) as a young man before turning his sights on a political career. In a campaign fought largely from a hospital bed (the result of a car accident in which he was injured), Pawley was elected in the provincial election of 1969 that brought Ed Schreyer and the NDP to power in Manitoba. During his time as a cabinet minister in the Schreyer administration, Pawley oversaw what would be the first of many contentious issues that would mark his years in office, including the introduction of public auto insurance (commonly referred to as Autopac) in Manitoba. This would be followed by battles concerning French-language rights in the province, changes to Manitoba's labor, marriage, and human rights legislation, and, on the national stage, the proposed free trade agreement between Canada and the United States.
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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 | 0.002 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.006 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.005 | 0.006 |
| Insufficient payload (model declined to judge) | 0.034 | 0.030 |
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".