Minority Minsters in Media: A Study on Digital Representations of Canadian Sikh Politicians in Mainstream Media and Their Effects on Race Relations in Canada
Bibliographic record
Abstract
Focusing on Harjit Singh Sajjan and Navdeep Singh Bains of the Trudeau Administration, this project analyzes the effects of popular digital representations of Canadian Sikh Ministers, in daily news, on race relations in Canada, as quantified by representations of hate crimes. These representations are analyzed qualitatively and quantitatively; this study looks to how many representations in major media there are as well as what the specifics of certain representations do. This research draws on scholarly journals and theoretical articles for analysis; it uses them to determine the significance of specific representations and representations generally. It also examines primary sources, such as images from newspaper articles from the Globe and Mail, the Toronto Star, and Metro News about Minster Harjit Singh Sajjan and Minister Navdeep Singh Bains to discuss the importance of positive representations of Sikhs and explore how positive stereotypes are employed. The study finds that positive digital representations of diasporic communities in politics are key to evoking social change and affecting social life. Moreover, this study undermines the notion that political participation alone is sufficient to cause social change, as digital representation of participation is integral.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".