Bibliographic record
Abstract
In contemporary Canadian society, many people recognize Canada as multicultural while embracing the ideology of cultural mosaic, in which they welcome immigrants from various ethnic groups who bring their cultures and native languages to Canada. Vancouver is world-famous for being one of Canada’s most culturally diverse cities. This paper explores the manifestation of multiculturalism inside Vancouver’s Chinatown, where incessant marginalization of certain ethnic minority populations and rapid gentrification of the neighbourhood's periphery are currently taking place. My experience as a student researcher and volunteer at a small non-profit local community centre in the Downtown Eastside (DTES) neighbourhood has given me the privilege of conducting ethnographic research with a group of Cantonese-speaking Chinese seniors through the organization’s cultural heritage preservation program. Throughout my fieldwork, I discovered the success that this heritage program has in promoting and fostering multiculturalism and language diversity in the Canadian society, where English is the country’s dominant language. In this paper, I provide an exploration of how the heritage program has alleviated social, cultural, and language tensions among people from different racial and ethnic backgrounds. I argue that multilingualism is not only an indispensable component of Canada’s cultural mosaic but also the country’s promotion of multiculturalism.
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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.034 | 0.006 |
| Scholarly communication | 0.007 | 0.001 |
| Open science | 0.001 | 0.008 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.011 | 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".