Playoff Losses, Mayoral Politics, Image Repair, and Inoculation: Open Letter Sport Communication
Bibliographic record
Abstract
In May 2021, the Toronto Maple Leafs National Hockey League team lost a seventh and final playoff game—again. The day after their loss, Toronto Mayor John Tory took the unusual step of penning an open letter to Maple Leaf fans in response. His two-page letter was a unique mix of communication genres, including sport communication, political communication, and, with multiple references to COVID-19, health communication. It was also, as we argue here, a unique example of image repair rhetoric in general and sport image repair rhetoric in particular. In this rhetorical analysis, we build on a growing body of sport image repair in the form of open letters, revealing how the interaction of these contexts with Tory’s main focus on the team reveals how his open letter is at a crossroads of intersecting image repair efforts in politic, health, and sport. We draw three primary findings from our analysis, including the possibility that Tory’s letter functioned as an inoculation message, preparing fans to resist discouragement and a dampening of support.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.005 | 0.017 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.015 | 0.018 |
| Scholarly communication | 0.011 | 0.005 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.008 | 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".