The Handbook of Ethnic Media in Canada
Bibliographic record
Abstract
Ethnic minority groups in Canada have set up their own communication infrastructure that has evolved over time from the analog to the digital age, and continues to remain relevant across generations. Offering a reassessment of contemporary media outlets, The Handbook of Ethnic Media in Canada asks how ethnic media have changed, why they continue to be relevant, and what impact this media sector has on ethnocultural communities as well as broader society. Building on past studies that highlight particular functions of ethnic media – publishing information that is vital to settlement and civic engagement and providing an alternative to mainstream media, among others – this volume generates insights on new dynamics of the ethnic media sector that are prevalent in the digital age. Contributors re-examine theoretical and methodological approaches to ethnic media research, explore the practices of ethnic media along cultural, linguistic, and religious lines, and interrogate the policies that affect ethnic media production and consumption. At its core, the question of how Canadians engage with ethnic media is a question about what this media sector means for the sociocultural, economic, and political integration of Canadians, both majority and minority, and Canada’s race relations. The Handbook of Ethnic Media in Canada provides a rich resource for anyone concerned about the role media plays in the complex relationship between ethnicity, race, belonging, and marginality.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.005 | 0.012 |
| Science and technology studies | 0.011 | 0.004 |
| Scholarly communication | 0.008 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.039 | 0.011 |
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".