Stigma and its Moments: Intermediaries’ Use of Rituals With Audiences in Stigmatized Activities
Bibliographic record
Abstract
How do intermediaries at the boundaries of stigmatized organizations engage with their audiences to manage stigma? Despite the growing interest in organizational stigma, existing research often overlooks the critical moments of interaction where stigma is negotiated in real time. This study addresses this gap by examining the unique role of gambling attendants in Nigeria, who operate at the intersection of stigmatized organizations and their communities. Using an inductive, multi-site case study, this research reveals how intermediaries deploy rituals as powerful tools to navigate audience perceptions and transform the stigma associated with their work. The findings highlight the dual purposes of rituals: reinforcing social cohesion through practices like forecasting and resisting stigma by integrating non-gamblers into the social dynamics of the kiosks. By generating emotional energy that redefines stigmatized spaces, these rituals enable intermediaries to sustain audience engagement and mitigate marginalization. This study advances organizational stigma literature by shifting the focus from macro-level strategies to the micro-level interactions of intermediaries, while also contributing to the rituals literature by showcasing how rituals operate in high-stigma environments. For managers, the findings underscore the importance of boundary actors and the potential of ritualization as a strategy for fostering legitimacy in challenging contexts. Keywords: Stigma, Rituals, Interactions, West Africa
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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 | 0.003 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.007 | 0.008 |
| Scholarly communication | 0.006 | 0.004 |
| Open science | 0.001 | 0.009 |
| Research integrity | 0.002 | 0.002 |
| 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".