Bibliographic record
Abstract
By the turn of the twenty-first century, toleration of cultural difference or multiculturalism, broadly defined, became a hallmark of liberal democratic countries. As the first country to declare multiculturalism national policy in 1971, Canada became a world-renowned example. Once the target of forced assimilation or feared as the cause of separatism, diversity came to be embraced as part of Canada's official national identity and Anglo-individualist liberal heritage and structure. However, state supported multiculturalism in Canada and elsewhere has been met with mixed success, a fact which has been made clear by social and political movements which have sought to change or reject its terms. In some cases, these calls are from representatives of the constituencies whose grievances the model was devised to address. It is in this fraught context that some critics declare state multiculturalism's bankruptcy and demand a return to a more homogenous approach, while others suggest expanding or radicalizing the framework's terms. Why has multiculturalism failed to deliver its promise and what comes after it in Canada? A robustly democratic understanding of the constitutive diversity and sovereignties sharing the territory of Canada requires, I argue, that we go beyond the state-centred approaches critics of multiculturalism have challenged. As a political theorist, I respond to this question by looking to the historical context in which claims to group difference arise. Methodologically combining empirical and conceptual analyses, I principally focus on the illustrative and understudied communal Russian Doukhobors and the Indigenous Sinixt. I use this richly layered case of intersectional politics to focus attention on the democratic practices of Indigenous and immigrant communities as they press their claims in a triangulated relationship to one another and the state. Drawing on Hannah Arendt's conception of politics as "world-building," this case helps us to reframe national challenges of plurality from the limited abstract view of the state to the expansive view of the participatory democratic realm.
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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.030 | 0.036 |
| Scholarly communication | 0.013 | 0.004 |
| Open science | 0.001 | 0.009 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.008 | 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".