If Memes Could Dance: A Case Study of Rachael Gunn and the 2024 Olympics Breakdancing Scandal
Bibliographic record
Abstract
The purpose of this case study was to examine how Rachael Gunn and the Australian Olympic Committee (AOC) employed situational crisis communication theory and image-repair strategies in their responses to the 2024 Olympics breakdancing scandal. Furthermore, we examined how social media commentary responded to these crisis communication efforts. Gunn’s statement was primarily rooted in the image-repair strategies of bolstering , attack accuser, and victimization , while the AOC’s statement was rooted in the situational crisis communication theory strategies of denial , attack accuser , and reminder . Four key themes were found via an inductive analysis: Olympics , support/attack , performance , and Australia. Both Gunn and the AOC appeared to abide by scholars’ recommendations for leveraging both image repair and situational crisis communication theory in their reputation management efforts. However, the limited powers of persuasion are evident in the debates and discussions that emerged via social media commentary. We see this in the conflicting diatribes of support and condemnation, ridicule and empathy, joy and shame, and trust and doubt in response to Gunn’s performance and subsequent statements.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.006 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".