Bibliographic record
Abstract
Canadian demographic trends indicate that the number of religious adherents from various faith groups is on the rise. Despite successful integration of some religions into mainstream Canadian society, discrimination against some faith groups persists. Christianity is the dominant religion in Canada, the minorities being Islam, Judaism, Buddhism, Hinduism, and Sikhism. The mainstream media are considered a main driver of social cohesion in Canada because they construct ideologies and define communities. They are a key lever in shaping debate about religion in the public sphere; however, debates exist on how religion is portrayed in the media. Despite the vast religious diversity in Canada, media organizations commonly ignore religious minorities, deeming them insignificant, unfavourable, and sometimes invisible. This chapter reviews and compares research findings on Canadian media depictions of these faith groups over the past few decades. Canadians of various faith groups have expressed a wide array of sentiments toward their representations in the media. Vast differences in media depictions exist; however, dominant discourses and representations prevail for each faith group: Christians are the normal group; Muslims are in discord with Western societies; Jews require sympathy; Buddhists are peaceful; Hindus are friendly; and Sikhs are extremists. It is suggested here that considerable research needs to be conducted on Canadian mainstream media patterns of coverage and portrayals of interfaith activities within Canadian society.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.008 |
| Science and technology studies | 0.010 | 0.002 |
| Scholarly communication | 0.006 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.020 | 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".