Racism and Democracy Debates in the Canadian House of Commons
Bibliographic record
Abstract
This chapter employs empirical critical race theory (eCRT) to analyze the discussions of racism in the speeches and debates in the Canadian House of Commons. First introduced by Obasogie in his 2014 book, Blinded by Sight, eCRT uses a combination of critical race theory (CRT) and empirical research methods during analysis. Typically, CRT employs narratives, stories, and the different understandings of race and racism while empirical research relies on social science methods, creating a rigorous system of analysis that includes data and the lived experiences of others. eCRT is employed in this chapter to help frame the analysis of the different speeches and debates that occurred in the Canadian Parliament. To conduct the analysis, transcripts of speeches given in the House were collected and a mixed method approach was used to analyze them. The results show that there are prevalent topics that revolve around the issues of racism and democracy including: Indigenous people, Canadian Sikhs, Japanese Canadians, terrorism and Muslim Canadians, Israel and antisemitism, and hate propaganda. We argue that these House debates provide insight into and offer an understanding of what is important in relation to democracy and racism in a multicultural country such as Canada and examine how often certain issues have been discussed by federal Canadian politicians throughout the previous decades. Instead of being thematic or ongoing discussions in the Parliament to disrupt systemic racism, we find that the relevant debates tended to be episodic, intermittent, and centered on certain communities that have been historically impacted by racist policies.
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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 | 0.004 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.004 | 0.006 |
| Science and technology studies | 0.063 | 0.020 |
| Scholarly communication | 0.010 | 0.002 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.002 | 0.003 |
| 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".