Bibliographic record
Abstract
This content analysis study examines the lyrics contained in the fight songs of the 130 NCAA Division I Football Bowl Subdivision schools. Because fight songs are still being written and other fight songs are being updated to account for societal changes, a study of the themes that are common among fight songs would be valuable to those responsible for writing these important works. Literature related to college fight song studies, music, and branding, as well as music in advertising provides context to the study. The researchers engaged in a two-step process that involved theme identification and coded theme count. In the theme identification stage, the researchers used a common sample of two fight songs per conference to identify themes that consistently appeared in the song lyrics. The researchers then coded the full population of 130 songs seeking the identified themes across all songs. The most common themes were self-reference to the name of the university (97.7%), exclamation (93.1%), and togetherness (90%). The thematic analysis confirms the unification and excitement purposes that fight songs are intended to generate and confirm the role fight songs play in intercollegiate athletics branding— selling the concepts of unification and excitement to college sport consumers. The remaining themes included game-specific references, nickname, school colors, victory, vocalization, war, and word-splits.
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 | 0.001 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.006 | 0.006 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.471 | 0.420 |
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".