Reconstitutionalizing Multiculturalism: Governance Pathways for the Twenty-first Century
Bibliographic record
Abstract
This book has addressed a singular challenge: to explore, analyze, and compare the politics of multiculturalism as a complex of ideologies, policies, and programs in advancing an inclusive multicultural governance. All of the countries under this cross-national study—Canada, the United States, the Netherlands, Britain, Australia, and New Zealand—have demonstrated a propensity toward the principles and practices of multiculturalism. Each has also committed to actively depoliticizing the politics of difference by utilizing the principles of multiculturalism for securing governance goals in a politically acceptable manner. In some cases, multiculturalism is formally expressed at national or state levels. In other cases, for example in Europe where no country has adopted multiculturalism as an official policy (Phillips and Saharso 2008), multicultural responses are unofficial and indirect, manifest at local and regional levels, and reflected in initiatives that are multicultural in everything but name. Whether named or not, formal or informal, direct or indirect, the conclusion is inescapable: multiculturalism as a set of governance ideals—and policies and programs for transforming these ideals into practices (from antiracism to employment equity)—rarely wavers from its central mission. That is to make society safe from difference, yet safe for difference by improving the process of minority integration while neutralizing the salience of ethnocultural differences as sources of disadvantage or divisiveness (Eisenberg 2002). These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.
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 | 0.006 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.010 | 0.039 |
| Scholarly communication | 0.014 | 0.012 |
| Open science | 0.001 | 0.010 |
| Research integrity | 0.002 | 0.005 |
| Insufficient payload (model declined to judge) | 0.005 | 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".