Bibliographic record
Abstract
Multiculturalism had its beginnings as a public policy in Canada and Australia. It supplanted earlier policies, which aimed at assimilating both indigenous peoples and immigrants to create a homogeneous and cohesive population. Those earlier policies had provoked hostility from indigenous peoples, who had begun to call for recognition of their cultural distinctiveness and redress of historical wrongs. They also led to disaffection among immigrant groups, who expressed a strong desire to hold on to some, if not most, of their customs, their cultural traditions, and their languages. Some groups went further to argue that differences should not only be tolerated but also preserved and protected. The central issue in the theoretical debates that ensued has been whether, how, and to what extent diversity should be embraced. Much of that debate has taken place against the backdrop of broadly liberal thinking, for the problem of multiculturalism has been an issue primarily in liberal democracies. There are two questions that need to be resolved. The first is whether minority groups ought to be given cultural protection. The second is whether minority groups ought to be required or made to conform to (at least the most important) ethical standards of the majority society.
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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.006 | 0.007 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.014 | 0.003 |
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".