Not the Michigan State University of Old? A Critical Discourse Analysis of Crisis Communication
Bibliographic record
Abstract
The purpose of this study was to examine how Michigan State University (MSU), Mel Tucker, and Brenda Tracy engaged in crisis communication and image repair amid an ongoing crisis involving allegations of sexual assault. Specifically, this work moved beyond identifying the crisis-communication strategies being employed, answering the call to employ critical discourse analysis in scholarly examinations of crisis-communication efforts. MSU primarily employed bolstering (reminder) rooted in procedural discourse and corrective action rooted in discourse(s) of organizational change. Mel Tucker leveraged both rape myth and witch-hunt discourses in his utilization of attack accuser, victimization, and minimization (excuse). Tracy countered his tactics by employing discourse practices associated with the broader “Me Too” movement with her use of victimization, shifting blame, and attack accuser. This work illuminates the nuances of crisis communication beyond the strategies being employed, revealing how the strategies are linked to broader societal discourses. Furthermore, the study demonstrates that discourses and ideologies present in society at large manifest in the crisis-communication efforts of sport organizations and individuals, thereby providing a worthwhile contribution to the corpus of literature pertaining to crisis communication in sport.
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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 | 0.003 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.012 | 0.008 |
| Scholarly communication | 0.008 | 0.007 |
| Open science | 0.000 | 0.003 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.005 | 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".