Of Headlamps and Marbles: A Motivated Perceptual Approach to the Dynamic and Dialectic Nature of Fairness
Why this work is in the frame
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Bibliographic record
Abstract
How do people perceive fairness? Recently, fairness scholars have raised important theoretical questions related to what information is used in fairness perceptions, why this information is emphasized, and how fairness perceptions can change over time. Integrating the Brunswikian lens approach with a motivated cognition perspective, we develop the Motivated Perceptual Approach (MPA) to highlight how people can be motivated to selectively perceive and weight cues to form fairness perceptions that align with their motives. However, these motives can change over time and through interaction with motivated others. By illuminating the dynamic and dialectic processes underlying fairness perceptions, the MPA sheds light on how people's fairness perceptions can be influenced by their own motives as well as socially constructed and negotiated through interactions with motivated others. Practical insights include how to effectively manage fairness perceptions over time and across perspectives. We conclude with a research agenda for advancing the fairness literature. Plain Summary Whether or not people perceive they (or others) have been treated fairly or are treating others fairly at work, has implications for a variety of important outcomes ranging from helping others (when people perceive fairness) to undermining supervisors, making plans to quit or punishing bad actors (when people perceive unfairness). Important questions remain, however, around how people come to these perceptions in the first place. How do they decide what is fair? A long time assumption has been that these perceptions are subjective and motivated; that “fairness is in the eye of the beholder.” Based on this assumption, two people who experience the same event may come away with very different fairness perceptions. This is a crucial insight that helps explain the significant disparities in perceptions of fairness between people. However, as a field, we seem to have strayed from that foundational assumption. In this paper, we revisit this premise to develop an approach describing how people collect and integrate information to inform their fairness perceptions, highlighting the particular role that their motives (what they want to perceive, e.g., that they are fair actors, that they are treated well by important others) shape what information they attend to and use in arriving at their perceptions of fairness. From this perspective we explain how fairness perceptions can change over time, explain and predict differences between perspectives (e.g., managers and employees), and provide guidance for developing practical interventions that can reduce these differences before they become intractable.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it