An Examination of Michigan State University’s Image Repair via Facebook and the Public Response Following the Larry Nassar Scandal
Bibliographic record
Abstract
The purpose of this study was to examine how Michigan State University (MSU) utilized Facebook as a tool for image repair following the Larry Nassar sex abuse scandal. Specifically, the researchers were concerned with the image-repair approach utilized by MSU during Nassar’s hearing and in its immediate aftermath. Additionally, the researchers examined users’ responses via Facebook comments to determine reactions to MSU’s image-repair strategies. MSU primarily employed the image-repair tactic of corrective action along with rallying, bolstering, and mortification. Overall, individuals posting comments did not appear to buy into MSU’s image repair. Users focused blame on MSU for mishandling the situation and discussed various aspects of the Nassar case as well as MSU’s mistreatment of the victims. Additionally, there was a call for MSU to change its culture, take ownership of its mistakes, and become a leader in dealing with sexual assault on campus.
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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 | 0.004 | 0.016 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.007 | 0.003 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".