Bibliographic record
Abstract
In so-called post-factual societies, where public debates are undermined by their false or misleading premises, philosophers who have reflected on diversity and pluralism can offer a critical and clarifying perspective through which to evaluate the statements of politicians and the media. Félix Mathieu offers a theoretical, empirical, and normative analysis of the debates surrounding the accommodation of ethnocultural and societal diversity in contemporary liberal democracies. With a close lens on Canada, he looks at case studies in the United Kingdom and the Netherlands to test political leaders’ and analysts’ claims of successful accommodation and pluralism. Taking Pluralism Seriously provides a clear, fair, and helpful summary of the debate so far in order to understand the promises and pitfalls associated with theories of multiculturalism, interculturalism, federalism, and multinational democracy, investigating the conditions that might make it possible for different national communities to become fully empowered, politically and culturally. Taking Pluralism Seriously invites readers to explore questions of pluralism and accommodation and proposes political reforms to meet the challenges arising from diversity, while considering some of the most pressing concerns complex societies are facing today.
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 | 0.005 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.011 | 0.027 |
| Scholarly communication | 0.011 | 0.014 |
| Open science | 0.001 | 0.007 |
| Research integrity | 0.004 | 0.011 |
| Insufficient payload (model declined to judge) | 0.006 | 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".