Bibliographic record
Abstract
ABSTRACT There are lively debates about multiculturalism in Canada and Europe, but the term is used much less frequently in the United States, where instead there are arguments about ‘identity politics’, how history is taught and how the citizenry views their country. I argue for what I call multicultural integration, which accepts the inevitability of group identity and that we live in a world of nation‐states; its aim is for a loosely defined and changeable national identity, one that can incorporate new groups. Although I argue that multicultural integration has been largely successful in the United States, it has little to say about race, which has limited its impact. This is not a theoretical flaw since multicultural integration does not aim to be a comprehensive theory of justice. For members of the progressive left, however, much of multiculturalism does not press for enough change. Conservatives have responded to the social justice arguments with claims that such talk is divisive and even treasonous. I point out some of the flaws in each position and contend that one key advantage of multicultural integration is how it explicitly recognizes groups and boundaries and works to make them porous and relatively tame.
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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.019 | 0.012 |
| Scholarly communication | 0.010 | 0.002 |
| Open science | 0.001 | 0.011 |
| Research integrity | 0.004 | 0.005 |
| Insufficient payload (model declined to judge) | 0.005 | 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".