A Pragmatic Study of Image Restoration via Corporate Apology in Chinese Internet Corporations
Bibliographic record
Abstract
With the booming of Chinese internet corporations, various wrongdoings have been frequently exposed to the public, which damages their corporate image. To face the challenge, these companies usually resort to apologies for image restoration. This study investigates how apology strategies are employed by Chinese internet corporations to restore image in the event of wrongdoings. Based on a self-built corpus and by means of textual analysis, we identified different apology strategies characterized by various linguistic features. The results show that “Illocutionary Force Indicating Devices (IFIDs)” and “damage repair” are two of the most frequently used move types which are normally marked by such key linguistic features as personal pronouns, modal verbs, performative verbs and intensifiers. It is also found that IFIDs, “giving account” and “admitting mistakes”, “offering repair” and “inviting further interaction” are often incorporated together to show the company’s sincere apologetic stance which contributes to the ultimate goal --- rebuilding corporate image and regaining the public’s trust. However, direct expressions of “asking for forgiveness” are seldom found in apologies crafted by Chinese internet corporations. This study on apologies in the domain of internet corporations is believed to shed light on research on corporate apology in particular and corporate image restoration in general.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.004 | 0.004 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".