The social construction of fairness: social influence and sense making in organizations
Bibliographic record
Abstract
Abstract This paper explores how the social relationships employees have with peers and managers are associated with perceptions of organizational justice. These relationships are theoretically modelled as the conduits for social comparison, social cues, and social identification, which are sources of sense making about fairness ‘in the eyes of the beholder.’ It is argued that perceptions of procedural and interactional justice are affected by this type of social information processing because: (1) uncertainty exists about organizational procedures; (2) norms of interpersonal treatment vary between organizational cultures; and (3) interpersonal relationships symbolize membership in the organization. A structural equations model of data from workers in a telecommunications company showed that an employee's perceptions of both procedural and interactional fairness were significantly associated with the interactional fairness perceptions of a peer. In addition, employees' social capital, conceived as the number of relationships with managers, was positively associated with perceptions of interactional fairness. In the structural model, both procedural and interactional justice were themselves significant predictors of satisfaction with managerial maintenance of the employment relationship. The discussion highlights the key role which the fairness of interpersonal treatment appears to play in the formation of justice judgements. Copyright © 2001 John Wiley & Sons, Ltd.
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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 | 0.007 | 0.020 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.003 | 0.012 |
| Scholarly communication | 0.006 | 0.002 |
| Open science | 0.000 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".