“We Are Deeply Sorry”: Chinese Corporate Apologies Posted on Weibo
Bibliographic record
Abstract
The present study focuses on the components of Chinese corporate apologies by adopting both quantitative and qualitative methods. The investigation is based on 25 corporate apology statements posted on Weibo, one of the most popular social media platforms in China. The results indicate that two apology components, namely explicit apology and offer of repair, are more salient than others, and more than half of the apology statements contain the components of explanation and taking on responsibility, while the use of another two components which include promise for forbearance and expression of sorrow depends on the severity of the offensive act. In addition, apologies made by Chinese companies via Weibo are realized through various linguistic means, such as IFIDs, intensifiers, commissives, appraisal resources, presupposition and metadiscourse (attitude markers, boosters and code glosses). Hopefully, this study might provide insight into future research on corporate apologies and how to restore corporate images in crisis communication.
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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 | 0.002 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".