Spirals of Silence: The Dynamic Effects of Diversity on Organizational Voice*
Bibliographic record
Abstract
ABSTRACT When will individuals speak up about organizational issues, and when will they remain silent? We suggest that organizational voice will be significantly influenced by individuals’ perceptions of the attitudes towards an issue within their workgroup. In particular, individuals will be more likely to speak up when they believe that their position is supported by others, and remain silent when they believe that it is not. We explain this using the ‘spiral of silence’ proposed by Noelle‐Neumann (1974 , 1985 , 1991 ) and widely used in public opinion research, which explains how majority opinions become dominant over time and minority opinions weakened. Spirals of silence within groups can restrict the open and honest discussion that is essential to organizational improvement. Noelle‐Neumann's spiral of silence emphasizes the horizontal pressures that the threat of isolation and corresponding fear of isolation exert to keep people from being open and honest about their opinions. We argue in this paper that the fear and threat of isolation are particularly powerful for members of invisible minorities such as gay and lesbian employees. We propose a second, vertical ‘spiral of silence’ may develop through processes at a more micro level within the workgroup and organization. This second spiral begins with the inability to fully express one's personal identity within the workgroup because of a negative climate of opinion towards a particular aspect of one's identity. This may be especially true for ‘invisible’ sources of diversity such as sexual orientation. Revealing a potentially disruptive identity might impair social cohesion: concealing it, however, can inhibit social exchange and task exchange and reduce self‐efficacy, leading to organizational silence. However, an alternate virtuous spiral can result in which individuals will feel empowered to express organizational voice.
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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 | 0.006 | 0.043 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.005 |
| Scholarly communication | 0.006 | 0.004 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.001 | 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".