Building Leafs Nation: rhetorical devices in the online imagined community of the Toronto Maple Leafs
Bibliographic record
Abstract
Established almost 100 years ago, the Toronto Maple Leafs hockey team is known for their large, dedicated fan base called Leafs Nation. I am a devoted member of this nation and, as such, the team’s communication practices speak both to me, and about me. This major research paper (MRP) is an analysis of e-mails sent to a subscriber-only list in the context of marketing. Researching the e-mail communication from the Toronto Maple Leafs to their fan base lends itself to an understanding of the communication that occurs in a professional setting. Not only do the Toronto Maple Leafs communicate directly with their proactive fan base but I argue that the way in which they do this instills a sense of community within Leafs Nation through the use of themes, metaphors and rhetorical tropes. Communicating effectively with a fan base is an essential component in running a sports organization. Texts in the form of words and images do not only assist in getting an organization’s message to the supporters, but their connotative meanings can also contribute to the senses of community and belonging. This paper will examine how the Toronto Maple Leafs employ rhetorical devices in the e-mail newsletters sent out to Leafs Nation, as well as analyzing the rhetorical connotations in these devices. Also, I will be examining the way in which the use of rhetorical devices contributes to the creation of an online ‘imagined community,’ a concept first introduced by Benedict Anderson in 1936 in the context of nations and nationalism. Anderson stated that an imagined community does not conform to traditional ideals of a community and is constructed by those that see themselves as being a part of this community, and I see the Leafs Nation as conforming to the ideals detailed by Anderson. As such, I will be completing a qualitative textual analysis of 43 e-mails that have gone out to the subscriber-only fan list since 2012. By examining these e-mails I will attempt to identify the presence of the rhetorical devices of pathopoeia, scesis onomaton and principle of scarcity and the overall frequency with which they appear. Based on the data that emerges from my research, I will then attempt to draw correlations between the findings and attempt to link the presence of rhetorical devices as a contributing factor to the creation of Leafs Nation as an online imagined community through a qualitative textual analysis.
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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 | 0.003 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.015 | 0.024 |
| Scholarly communication | 0.013 | 0.009 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 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".