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Record W4386481872 · doi:10.1177/20413866231199068

Of Headlamps and Marbles: A Motivated Perceptual Approach to the Dynamic and Dialectic Nature of Fairness

2023· article· en· W4386481872 on OpenAlexafffund
Michael Ramsay Bashshur, Laurie J. Barclay, Marion Fortin

Bibliographic record

VenueOrganizational Psychology Review · 2023
Typearticle
Languageen
FieldPsychology
TopicCultural Differences and Values
Canadian institutionsUniversity of Guelph
FundersSocial Sciences and Humanities Research Council of CanadaAgence Nationale de la Recherche
KeywordsDialecticPerceptionCognitive psychologyPsychologyComputer scienceSocial psychologyEpistemologyPhilosophyNeuroscience

Abstract

fetched live from OpenAlex

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.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.007
metaresearch head score (Gemma)0.011
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: Theoretical or conceptual
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.008
Threshold uncertainty score0.040

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0070.011
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0020.001
Science and technology studies0.0040.021
Scholarly communication0.0080.010
Open science0.0010.005
Research integrity0.0020.004
Insufficient payload (model declined to judge)0.0050.000

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.

Opus teacher head0.056
GPT teacher head0.386
Teacher spread0.330 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designTheoretical or conceptual
Domainnot available
GenreEmpirical

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".

Quick stats

Citations9
Published2023
Admission routes2
Has abstractyes

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