Bibliographic record
Abstract
Abstract The concepts of multiculturalism and multilingualism promoted in applied linguistics research embrace liberal ideology that challenges the taken‐for‐granted legitimacy of the dominant forms of culture and language. Ideological underpinnings of both concepts can be examined through an overview of the development of multicultural education in the United States and Canada, as well as the recent popularity of the inquiry into linguistic diversity. In the United States, multicultural education emerged during the civil rights era and has tended to superficially recognize and celebrate cultural differences. Although such liberal multiculturalism has been challenged by critical multicultural education, which pays closer attention to racism and other injustices, the critical approach has more recently been absorbed into a broader focus on education for social justice. In Canada, multiculturalism became a state policy in response to increased ethnic diversity during the time when official bilingualism in English and French was being established. Similar to the United States, multicultural education in Canada has tended to support a liberal approach, although critical orientations, especially repatriation of Indigenous people's culture and language, have recently emerged. In both American and Canadian contexts, multilingualism and multiculturalism have become increasingly complicit with neoliberal ideology that promotes diversity for pursuing capitalist interest. Concerning multilingualism, although the recent trend of the multi/plural turn valorizes linguistic diversity and fluidity, critical perspectives scrutinize how linguistic differences are reproduced and hierarchized by power. Furthermore, the multi/plural approaches to culture and language can easily become complicit with neoliberal ideology. Applied linguists should stay vigilant against repressive ideology.
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 | 0.004 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.008 | 0.042 |
| Scholarly communication | 0.010 | 0.005 |
| Open science | 0.001 | 0.007 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 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".