When Stigma Harms Legitimacy: Managing the Dynamics of Stigmatization and Legitimation
Bibliographic record
Abstract
When politico-religious audiences perceive the behavior of a multinational enterprise (MNE) as socially harmful, they stigmatize the MNE and threaten its legitimacy. While the negative economic ramifications of stigmatization have been studied, we know less about the process of managing the growing, damaging influence of ‘pockets of opposition’ and their labeling of MNEs. We fill this gap by investigating how a stigmatized MNE maintains its legitimacy against increasing opposition from stigmatizing audiences, using the case of a century-old subsidiary company of a Dutch beer-brewer in Indonesia, the largest Muslim-majority country, where alcohol consumption is proscribed. We articulate the dynamics of managing core-stigma in this environment and theorize the process of maintaining the MNE’s legitimacy when well-supported stigmatizers gain considerable influence and systematic and proactive intervention is required. An important part of this intervention is the way in which our MNE was able to draw on its longstanding knowledge of its industry to craft strategies addressing a major social issue, in turn supporting its legitimacy.
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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 | 0.013 | 0.037 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.015 | 0.032 |
| Scholarly communication | 0.013 | 0.013 |
| Open science | 0.002 | 0.014 |
| Research integrity | 0.006 | 0.006 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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".