Bibliographic record
Abstract
George Grant (1918-1988) was the grandson of men who shaped Queen's University and Upper Canada College (UCC), the son of one of UCC's most famous principals, the nephew of Vincent Massey, Canada's first native-born Governor General. Yet he did not become a prime minister of Canada as his mother had hoped. Instead, deeply affected by the violence of the Second World War, he became one of Canada's most original political and religious thinkers. His book Lament for a Nation led some to call him a Red Tory and name him as the dominant intellectual force behind the Canadian nationalist movement of the 1970s. George Grant saw both himself and the future of his country in a different light. A life-long pacifist who argued against Canada accepting nuclear missiles in the 1960s, Grant reminds us why such weapons need to be resisted now and evermore and what kinds of strength we need to keep Canada alive and lively in the face of the globalisation of everything, including terrorism. George Grant loved Canada. Mightily. But Canada he loved is more than a place - it is also a state of mind. For Grant, a large part of being a Canadian resides in an ability to remember lovingly things that other North Americans have forgotten or never known and to resist their destruction. With energy, affection, and insight, T F Rigelhof gives us George Grant: the public thinker who challenged conventional attitudes towards Quebec, national politics, justice, and the American empire; the brilliant teacher at McMaster and Dalhousie who sought to create new ways of studying the great thinkers who shaped us; and, the private man who could be both eminently loveable and infuriating.
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 | 0.004 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.031 | 0.014 |
| Scholarly communication | 0.011 | 0.005 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.009 | 0.013 |
| Insufficient payload (model declined to judge) | 0.016 | 0.002 |
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".