Bibliographic record
Abstract
Abstract The term “multiculturalism” was used as early as the 1940s in Canada (Glazer 1997). Originally articulated to define a particular theory of social policy which prized the recognition of ethnoracial heritage over acculturation, by the 1990s the term attained the widespread usage it enjoys today in discussions ranging from education and politics to academia. “Multicultural” has arguably become protean with overuse; this essay endeavors to bring specificity to this concept which is so crucial to our understanding of contemporary politics and society. In exploring the key themes which define the capacious theory of multiculturalism in the 20th and early 21st centuries, this essay focuses on the case of the United States. Negotiating the problems and potential of its multicultural populace has necessarily been at the forefront of the intellectual, policy, and pedagogical agenda of this self‐styled “nation of immigrants.” While the American case is exceptional, it raises vital questions of global concern in a world becoming unequivocally more diverse as vast immigration flows disrupt traditional notions of the nation state and of attendant conceptions of citizenship and its privileges.
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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.009 | 0.015 |
| Scholarly communication | 0.007 | 0.004 |
| Open science | 0.001 | 0.007 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.010 | 0.001 |
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".