Bridging Divides: How the Edmonton Heritage Festival Can Mitigate Ethnic Polarization and Build Social Trust
Bibliographic record
Abstract
Although Canada is often celebrated as a multicultural nation, the political reality of multiculturalism is more complicated than it appears. This paper explores how government-funded multicultural events, such as the Edmonton Heritage Festival, promote the building of social trust and mitigate ethnic polarization. Social trust plays an essential role in multicultural societies as its abundance fosters the cooperative attitudes necessary to maintain social order. The Edmonton Heritage Festival serves as a site where cultural visibility and intercultural contact promote mutual acceptance and understanding. Through attitude and behaviour altering, individuals are exposed to different cultures in ways that can gradually build trust. However, the festival also presents challenges, including the potential exoticization and objectification of cultures, especially those marginalized and racialized within Canadian society. The commercialization of the event may reduce its significance and reinforce orientalist stereotypes. Despite these tensions, the festival creates a space for increased cultural exposure, providing opportunities for people to come together and form shared understandings. As such, even if it is sometimes superficial, it still contributes to a more cohesive and inclusive society by allowing difference to coexist with respect.
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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 | 0.006 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".