Scandal in College Basketball: A Case Study of Image Repair via Facebook
Bibliographic record
Abstract
The purpose of this case study was to determine what image-repair strategies the University of Louisville employed immediately after the announcement of an FBI investigation involving multiple universities and college coaches taking bribes in order to steer high-profile recruits to certain agents. Specifically, this case study examined the image-repair strategies used on the University of Louisville’s official Facebook page and the comments made to those posts to gauge public reaction to the university’s image-repair strategies. The University of Louisville primarily employed the image-repair strategies of transcendence, bolstering, stonewalling, and a newly identified strategy referred to as rallying, or unifying and “moving beyond” the scandal. Three themes emerged from an inductive analysis of users’ comments, including support, rejection, and scandal. The high volume of support indicates that many users were receptive to the university’s attempt to reduce the offensiveness of the scandal through the use of bolstering and transcendence.
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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 | 0.006 | 0.016 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.027 | 0.010 |
| Scholarly communication | 0.007 | 0.006 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.006 | 0.005 |
| Insufficient payload (model declined to judge) | 0.003 | 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".