I am Sorry and Sincere: A Case Study of Influencer Apology
Bibliographic record
Abstract
Influencers have become prominent figures whose actions are closely watched by the public in the digital age. When scandals arise, they tend to make apologies on social media platforms, sparking discussions on their sincerity and credibility. Taking a pragmatic point of view, this study takes YouTuber Jenna Marbles’ apology as an example to explore the pragmatic strategies applied to increase sincerity of apology and rebuild trust. The study transcribed the apology video and applied Blum-Kulka and Olshtain’s framework of apology in its annotation and analysis. The findings reveal that the apology features a mixed use of explicit and implicit devices and that there are five strategies serving to enhance sincerity and rebuild trust: taking on responsibility, account of cause, offer of repair, promise of forbearance and apology intensification. The study seeks to enrich the pragmatics of apology literature and provide insights into the complex dynamics of public figures navigating crises in the digital era.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".