“Be a Good Fan During the Good, the Bad, and Even the Ugly”: Exploring Cultural Boundaries Through Sport Fan Discourses on Twitter
Bibliographic record
Abstract
While sport fandom can be assumed to be inclusive, a deeper exploration of discourses around fandom exposes alternative perspectives. Using the frameworks of cultural boundaries and critical discourse studies, we explored how sport fans use Twitter to create, maintain, and transform cultural boundaries of sport fandom. We used tweets from a season of the Toronto Blue Jays baseball team as a case. Data were collected via Visual Twitter Analytics software focusing on tweets containing #LetsRise and #BlueJays. From the larger data set, we selected 172 tweets to examine using critical discourse analysis and ideological structures of discourse. Findings demonstrate that discourses of loyalty, consumption, and unity have plural meanings and are used to draw boundaries that are simultaneously fluid and rigid. Thus, we argue that fans engage in an active process of determining who is and is not included in fan cultures through Twitter use.
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 | 0.006 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.009 | 0.013 |
| Scholarly communication | 0.009 | 0.009 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".