The impact of learning about historical and current injustices, individual racism, and systemic racism on anti-Indigenous prejudice
Bibliographic record
Abstract
Prejudice toward Indigenous Peoples in Canada is a pressing issue with often violent consequences. Education, including historical education, is one common strategy to challenge anti-Indigenous racism. Despite the existence of multiple education-based training programs designed to address racism, there is limited evidence that education can reduce prejudice. In this dissertation, I report the results of three studies that make up a mixed methods program of research. In Study 1, I interviewed eight Indigenous students at the University of Manitoba to understand their experiences with racism. Participants were most concerned with addressing ignorance and stereotypes about Indigenous people. In Study 2, I surveyed 3,011 students at the University of Manitoba to learn about Indigenous students’ experiences with racism on a broader scale and to learn about non-Indigenous students’ attitudes, social norms, and perceived behavioral control regarding learning about Indigenous issues. The results conceptually replicated those of Study 1 for Indigenous participants and indicated that non-Indigenous participants were interested in brief and interesting video-based learning opportunities. Based on these first two studies, I developed an educational intervention that addressed Indigenous and non-Indigenous students’ needs and interests. In Study 3, I experimentally assessed the effectiveness of this intervention through five conditions on non-Indigenous undergraduate participants’ Indigenous-related thoughts, feelings, knowledge, behavioral intentions, and behaviors over three time points. In the first condition, participants proceeded to the next part of the study with no intervention. In the second condition, participants viewed a brief educational video about historical and current injustices toward Indigenous people. In the third condition, participants viewed the same educational video as in the second condition and an individual racism video. In the fourth condition, participants viewed the same educational video as in the second condition and a systemic racism video. Finally, in the fifth condition, participants viewed the same educational video as in the second condition, the same individual racism video as in the third condition, and the same systemic racism video as in the fourth condition. Results indicated that learning about historical and current injustices indirectly increased pro-Indigenous behaviors and directly shifted Indigenous-related thoughts, feelings, knowledge, and behavioral intentions. Learning about racism as an individual phenomenon, systemic phenomenon, or both, yielded unique effects on the Indigenous-related thoughts, feelings, and behavioral intentions. I discuss the implications of this mixed methods program of research for prejudice reduction researchers.
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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 | 0.002 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.004 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".