Contributions and Challenges of Addressing Discursive Racism in the Canadian Media
Bibliographic record
Abstract
Over the last three decades, our research has largely focused on the social systems that contribute to and reinforce racism in Canadian society. The media are among the most powerful of these many institutions, as they help transmit its central cultural images, ideas, and symbols as well as a nation’s narratives and myths. Media discourse plays a large role in reproducing the collective belief system of the dominant White society and the core values of this society. Using discourse analysis as a central tool, we have analyzed how social power, dominance, and inequality are produced and resisted through text and talk. The coverage of issues affecting racialized minorities is filtered through the stereotypes, misconceptions, and erroneous assumptions of a largely White-dominated group of media institutions. The media’s images reinforce cultural racism and White hegemony. Our approach identifies a constant and fundamental tension between the everyday experiences of racialized and indigenous people and the perceptions of publishers, editors, journalists, producers, broadcasters, and other media personnel, who have the power to redefine that reality. Over the years, we have continued to document the ways in which racism as ideology, policy, and praxis functions in media organizations. In all of our research and writing, we note how so-called liberal ideologies carry very different meanings, connotations, and consequences. We believe that notions of tolerance, accommodation, equality, fairness, and freedom of expression—central concepts in liberal media discourse—have immensely flexible meanings. Our work has been influenced by many scholars of discourse analysis, such as Teun van Dijk, Michel Foucault, and Stuart Hall. The framework we share is the belief that racialized discourse advances the interests of White hegemony and has an identifiable repertoire of ideas, words, images, and practices through which racism is advanced.
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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 | 0.040 | 0.067 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.011 | 0.009 |
| Science and technology studies | 0.056 | 0.046 |
| Scholarly communication | 0.045 | 0.017 |
| Open science | 0.008 | 0.017 |
| Research integrity | 0.007 | 0.009 |
| Insufficient payload (model declined to judge) | 0.012 | 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".