Bibliographic record
Abstract
Taking the activity of Gaelic Athletic Association (GAA) fans across the Twitter microblogging platform as a case study, this article examines GAA fans’ collaborative output as a form creative writing that bears resemblance to the Gaelic oral storytelling tradition known as seanchas[i]. Traditionally, Gaelic custom was rooted in the oral transmission of culture in which there was a great reliance on seanchas and the bardic tradition. Web 2.0 technology has provided new modes of dissemination of culturally significant stories across society. Twitter is an example of a platform that enables collaborative production and creative dialogue across communities: it can be viewed as an open mic of sorts that substitutes as the “céilí”[ii], a term for a traditional Gaelic gathering. In considering sport as storytelling, this research views fans as both readers and writers of the game and explores the latter in terms of how fans on Twitter collaborate through creative writing in the reinterpreting, retelling, recreating and reimagining of Gaelic games. Taking an interdisciplinary approach, it examines fan-generated narratives with a focus on two key areas: (1) how the growing prevalence of “in-play” discourse enables the production of fanfiction by GAA fans that bears resemblance with the seanchas tradition, and (2) how the reflexive role of fans as consumers and producers results in the creation of sports narratives of cultural authority.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.002 |
| Scholarly communication | 0.008 | 0.005 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.041 | 0.007 |
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".