Rhetorical legitimation strategies and sport and entertainment facilities in smaller Canadian cities
Bibliographic record
Abstract
Research question: This study explores the rhetorical legitimation strategies employed by proponents to justify their support for publicly funding arena construction within their respective communities.Research methods: Fifty-six semi-structured interviews were conducted with local stakeholders in eight Canadian cities. Participants included city employees (city managers, parks and recreation, tourism), elected officials, arena management, members of chambers of commerce and local business associations, prominent members of the local business community, other politicians (members of parliament), and other relevant stakeholders.Results and findings: Data analysis identified key rationales employed by proponents in order to justify arena development projects in their cities. These are then discussed in terms of the rational, value-based, and authority-based arguments employed to build and maintain pragmatic, moral, and cognitive legitimacy.Implications: The legitimation strategies highlighted in this study demonstrate how proponents were able to frame their arguments to build these facilities in a manner that would promote their goals for the city. In particular, the study highlights how this process plays out in smaller cities where the decision to build the facility has already been made.
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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 | 0.009 | 0.025 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.005 | 0.005 |
| Science and technology studies | 0.026 | 0.024 |
| Scholarly communication | 0.014 | 0.003 |
| Open science | 0.003 | 0.008 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.007 | 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".