Bibliographic record
Abstract
While the role of the university president has evolved dramatically in recent years, the recruitment pool and selection process have changed little since the 1960s. In Leadership Under Fire, Ross Paul combines leadership theory, interviews with eleven of Canada's most successful presidents, and thirty-five years of personal experience to shed light on the complexity and importance of leading a university and identifies some of the critical challenges and opportunities facing Canadian universities today. Paul illuminates some of the ways in which Canadian universities are unique and uses these differences to make clear the importance of organizational, cultural, and institutional fit for leaders confronting critical academic issues such as academic leadership and accountability, student success and support, university funding and fund-raising, strategic planning, government and community relations, and internationalism. His analysis reaffirms some long-standing practices, while arguing that changes are badly needed in others. While much has been written about university leadership elsewhere, Leadership Under Fire focuses on Canada and some of the men and women who have made a real difference to the quality of its post-secondary institutions. Paul builds on their stories to offer useful perspectives and advice at a time when the quality of universities was never more critical to the country’s economic, social, and political success.
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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.005 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.068 | 0.037 |
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".