Showcasing a Student-Led Anti-Racism Virtual Simulation Module for Equitable Learning
Bibliographic record
Abstract
Background: Misinformation and disinformation perpetuate negative stereotypes, reinforce prejudice and lead to racially motivated discrimination. As displayed in post-secondary institutions worldwide, white-centric perspectives are primarily embedded in school cultures and systems. Such racial aggressions produce ‘racial battle fatigue,’ creating physiological, physiological, and behavioural stress for the recipient. Virtual simulation is proposed as an educational strategy for learners to identify racism in an academic setting and develop allyship for equity-seeking groups. This poster aims to advocate the use of virtual simulation to improve the overall educational experience by highlighting the voices of diverse students and educators. Methods: Our team developed a pre-learning simulation consisting of five short scenes depicting racially discriminatory acts. It highlights the urgency to discontinue the term 'microaggression'. A longer simulation was developed in which a racially discriminatory act is depicted, with a bystander present. This longer simulation allows participants to adopt the role of a bystander and use the ARISE model to ally with the BIPOC community in addressing racism (overt and covert racism). The ARISE model encompasses five key elements: awareness, responding with empathy, inquiring about facts, using "I" statements, and educating and engaging. Intended Outcomes: We seek to provide information regarding racial inequities in higher education curriculum by: (1) illustrating how racism affects people in their learning environment, (2) demonstrating how bystanders might apply the ARISE model to help a person experiencing racism in an educational setting, and (3) learning how to be an ally.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.044 | 0.006 |
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".