;Take a Trip Around the World with Us!”: Thunder Bay’s Folklore Festival Narrates Whiteness, Culture, and Community
Bibliographic record
Abstract
The Folklore Festival is a widely-attended annual multicultural festival that takes place in Thunder Bay, Ontario. Branded as ‘a trip around the world in 48 hours’, the festival is lauded as an event that brings people together for the purpose of anti-racism.This dissertation explores how state-sanctioned multicultural events like the Folklore Festival disrupt, rework, or reinforce racial inequities. I contend that the Folklore Festival is a space of contradiction: it disrupts the demonstrated racial order in Thunder Bay by tolerating cultural displays from those who do not occupy white positionalities, but also reinforces the racial order of Thunder Bay by treating multiculturalism as something to be consumed by or produced for the white gaze. Guided by a decolonial feminist anti-racist praxis, I draw on 14 qualitative interviews with people who have organized, attended, or participated in the Folklore Festival to explore whether the festival is a space with emancipatory potential or simply another mode of reproduction of white hegemony. My analysis is supported by a theoretical foundation of critical race theory, cultural studies, ritual studies, and critical multiculturalism. My findings suggest that in its current iteration, the Folklore Festival – in line with the grander scheme of official multiculturalism – presents culture on a platter to be consumed or interacted with, but does not meet the mark as a site for anti-racist work. There are limited, if any, instances of “true learning” where attendees leave the festival having learned new information or experiencing a paradigm shift. Further, the Folklore Festival is presented as a celebration of difference, but that celebration appears to be in the interest of enriching a white majority as opposed to addressing the issues facing Black, Indigenous, and other visibly racialized peoples. This research interrogates what it means to celebrate multiculturalism fifty years after the inception of official multiculturalism policy and provides an understanding of how Thunder Bay’s racial order might be reproduced or disrupted in spaces meant to celebrate multiculturalism. Further, this work explores the implications for the quality of life that Black, Indigenous, and other visibly racialized people are able to have in the city.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.019 | 0.013 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.004 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".