Bibliographic record
Abstract
George Grant is widely regarded as one of Canada's most influential philosophers and political theorists.His best-known work, Lament for a Nation (1965), presented a radical reinterpretation of Canadian history and inspired a surge of nationalist sentiment across the country.Along with Grant's other books, it addressed the major cultural shifts and dilemmas of our age, and introduced several generations of students to the basic questions of political philosophy.This study aims to guide the reader towards a clearer understanding of Grant's thought.Focusing on his six short books and some of his most significant articles and speeches, Hugh Donald Forbes provides both an introduction to and an overview of Grant's career and his many contributions to the fields of political science, philosophy, religion, and Canadian studies.Throughout, Forbes sheds light on some of Grant's more contradictory and complex ideas, and provides an assessment of his impact on the Canadian political and cultural landscape.Forbes also relates Grant's work to that of three disparate and controversial European thinkers -Martin Heidegger, Leo Strauss, and Simone Weil -providing contexts and comparisons outside the strictly Canadian framework in which he is normally situated.Comprehensive and lucidly written,
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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.883 | 0.773 |
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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".