Bibliographic record
Abstract
At a time when too many of the world’s political leaders are consolidating power by playing on divisions and stoking fear, Unlikely Insider , a memoir by former federal cabinet minister and senator Jack Austin, comes as a welcome reminder of the value of public service as a force for economic progress, social justice, and nation-building. With both historical perspective and an eye to the future, Austin reflects on events and people whose impacts are still felt, and on the enduring challenges of Canadian life. Moving away from colonial domination of Indigenous Peoples, navigating our pivotal relationship with the United States and engagement with China, the nature and purpose of the Senate: these remain timely concerns, to which Austin has made significant contributions. Sharing insights into policy as well as into the personalities of colleagues and friends, Unlikely Insider paints vignettes of figures from Premier Zhou Enlai to Queen Elizabeth and recounts the author’s travels with Pierre Trudeau after the prime minister’s retirement. As a British Columbian, Austin worked to ensure that his province’s perspectives and interests mattered in Ottawa; as someone who came from a disadvantaged background, he is sensitive to the need to make the country a place of fairness and opportunity for all. Unlikely Insider reminds Canadians that inclusion – regional, social, and demographic – makes our nation both stronger and more just.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.022 | 0.012 |
| Scholarly communication | 0.015 | 0.010 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.004 | 0.007 |
| Insufficient payload (model declined to judge) | 0.054 | 0.015 |
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".