Bibliographic record
Abstract
Why do citizens in pluralist democracies disagree collectively about the very values they agree on individually? This provocative book highlights the inescapable conflicts of rights and values at the heart of democratic politics. Based on interviews with thousands of citizens and political decision makers, the book focuses on modern Canadian politics, investigating why a country so fortunate in its history and circumstances is on the brink of dissolution. Taking advantage of new techniques of computer-assisted interviewing, the authors explore the politics of a wide array of issues, from freedom of expression to public funding of religious schools to government wiretapping to antihate legislation, analyzing not only why citizens take the positions they do but also how easily they can be talked out of them. In the process, the authors challenge a number of commonly held assumptions about democratic politics. They show, for example, that political elites do not constitute a special bulwark protecting civil liberties; that arguments over political rights are as deeply driven by commitment to the master values of democratic politics as by failure to understand them; and that consensus on the rights of groups is inherently more fragile than on the rights of individuals.
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 | 0.008 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.010 | 0.051 |
| Scholarly communication | 0.013 | 0.016 |
| Open science | 0.001 | 0.012 |
| Research integrity | 0.003 | 0.009 |
| Insufficient payload (model declined to judge) | 0.009 | 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".