Bibliographic record
Abstract
In a seminal paper titled ‘Federalism and the Making of Nations’ first published in an edited volume of essays in 1955, Kenneth Wheare reflected upon the limits and possibilities of using the federal idea as a device for ‘bringing nations together, for preserving them and at the same time developing over and above their feelings of distinct nationality, a sense of common nationality’ (Wheare, 1962, pp. 29–30). In the same year, Pierre Trudeau, a future prime minister of Canada, also observed in a famous essay titled the ‘New Treason of the Intellectuals’ that by separating ‘once and for all the concepts of state and of nation’ it was possible to ‘make Canada a truly pluralistic and polyethnic society’ (Trudeau, 1968, p. 177). It was perfectly possible, in his view, for French Canadians to ‘lead the way toward making Canada a multi-national state’ (Trudeau, 1968, pp. 164–65). Clearly, both men believed that the relationship between federalism and nationalism was one that could be imaginative, constructive and innovative in the realms of practical government and politics, even if the likelihood was that such a project would require exceptional political wisdom and elite leadership skills together with a realistic acceptance that at the very outset instability would be immanent in the state.
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 | 0.006 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.011 | 0.013 |
| Scholarly communication | 0.005 | 0.006 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.006 | 0.000 |
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".