Multicultural Media in a Post-Multicultural Canada? Rethinking Integration
Bibliographic record
Abstract
This paper addresses the post-multicultural challenges that confront the integrative logic of Canada’s multicultural media. Multicultural (or ethnic) media once complemented the integrative agenda of Canada’s official multiculturalism, but the drift toward a post-multicultural Canada points to the possibility of a post-multicultural media that capitalizes on the positive aspects of multicultural media. The argument is predicated on the following assumption: an evolving context that no longer is multicultural but increasingly transnational, multiversal, and post-ethnic exposes the shortcomings of a multicultural media when applied to the lived-realities of those who resent being boxed into ethnic silos that gloss over multiple connections and multidimensional crossings. According to this line of argument, both diversity governance and ethnic media must reinvent themselves along more post-multicultural lines to better engage the transnational challenges and multiversal demands of a post-multicultural turn. Time will tell if a post-multicultural media can incorporate the strengths of a multicultural media, yet move positively forward in capturing the nuances of complex diversities and diverse complexities. Evidence would suggest “yes”, and that a post-multicultural media may well represent an ideal that reflects and reinforces the new integrative realities of a post-ethnic Canada.
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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 | 0.007 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.036 | 0.031 |
| Scholarly communication | 0.028 | 0.014 |
| Open science | 0.003 | 0.019 |
| Research integrity | 0.003 | 0.007 |
| Insufficient payload (model declined to judge) | 0.007 | 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".