Motivated cognition and fairness: Insights, integration, and creating a path forward.
Bibliographic record
Abstract
How do individuals form fairness perceptions? This question has been central to the fairness literature since its inception, sparking a plethora of theories and a burgeoning volume of research. To date, the answer to this question has been predicated on the assumption that fairness perceptions are subjective (i.e., "in the eye of the beholder"). This assumption is shared with motivated cognition approaches, which highlight the subjective nature of perceptions and the importance of viewing individuals arriving at those perceptions as active and motivated processors of information. Further, the motivated cognition literature has other key insights that have been less explicitly paralleled in the fairness literature, including how different goals (e.g., accuracy, directional) can influence how individuals process information and arrive at their perceptions. In this integrative conceptual review, we demonstrate how interpreting extant theory and research related to the formation of fairness perceptions through the lens of motivated cognition can deepen our understanding of fairness, including how individuals' goals and motivations can influence their subjective perceptions of fairness. We show how this approach can provide integration as well as generate new insights into fairness processes. We conclude by highlighting the implications that applying a motivated cognition perspective can have for the fairness literature and by providing a research agenda to guide the literature moving forward. (PsycINFO Database Record
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How this classification was reachedexpand
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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".