A few Weeks in Dirt City: Sport-Related Gentrification, Mobilizing Resistance, and the Art of Failure in Edmonton, Alberta
Bibliographic record
Abstract
There is no shortage of sociological research that explores the successes and failures of various sport-related social movements. However, a more capacious approach to understanding the significance of sport-related social movements, their imaginative actions and collective labor, and their impacts on social change, is one that shifts its focus away from binary categories of “success” and “failure”. In this paper, we explore the formation of the short-lived Edmonton Community Benefits Coalition, which emerged in 2016 to oppose the lack of a legally binding Community Benefits Agreement associated with a new publicly financed National Hockey League arena in Edmonton’s gentrifying city center, an area of spatially concentrated racialized poverty. Drawing from our ethnographic research, we examine how coalition members engaged in the collective labor of building solidarity, including the collaborative development of political strategies, while recognizing that the odds of successfully penetrating neoliberal capital and municipal governance were virtually impossible. Finally, given that the coalition ultimately “failed” to secure more significant institutional impacts, we offer an analysis of how this failure engendered several effects, including the cultivation of new relationships and political strategies in the ongoing struggle against gentrification and its related displacements in Edmonton, Alberta.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.027 | 0.021 |
| Scholarly communication | 0.006 | 0.001 |
| Open science | 0.003 | 0.006 |
| Research integrity | 0.002 | 0.003 |
| 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".