The Beginning of a Reckoning: An Application of Situational Crisis Communication Theory and Image Repair to the National Women’s Soccer League
Bibliographic record
Abstract
The purpose of this study was to examine the crisis of systemic sexual abuse, sexual harassment, and misconduct in the NWSL within the frameworks of situational crisis communication theory (SCCT) and image-repair theory (IRT). Specifically, this research engaged with three data sources (1) the Yates Report which details team by team allegations, (2) team statements in response to the allegations being made public, and (3) social media interactions in response to all statements. Based on the Yates Report, the Portland Thorns, Chicago Red Stars, and Racing Louisville (as well as the NWSL and USSF) were placed firmly within the preventable crisis cluster, which is marked by organization misconduct and management misdeeds. In terms of SCCT strategies, commonly used approaches were ingratiation and apology. In terms of image repair, commonly used approaches were corrective action, bolstering, and mortification, with corrective action being the most frequently utilized strategy across all statements. Within the preventable cluster, the two statements that received the highest interaction rates and like ratios were those that employed scapegoating, shifting blame, and corrective action. The implications of these findings as well as the utility of SCCT and IRT within the crisis communication landscape are discussed.
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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 | 0.005 | 0.016 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.003 | 0.007 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".