Inequality in People's Minds
Bibliographic record
Abstract
The extent of inequality that people perceive in the world is often a better predictor of individual and societal outcomes than the level of inequality that actually exists. As such, scholars from across the social sciences, including economics, sociology, psychology, and political science, have recently worked to understand individuals’ (mis)perceptions of inequality. Unfortunately, many researchers treat the process underlying such perceptions as a black box, focusing predominantly on lay people’s numeric estimates of inequality, and paying less attention to how people come to form these perceptions or what these perceptions mean to participants. In the current review, we draw on research in perception, cognition, and developmental and social psychology, to introduce a novel comprehensive framework for understanding individuals’ perceptions of inequality. We argue that subjective perceptions of inequality should be viewed as a process that unfolds across five interlinked and iterative stages. To form perceptions of the scope of inequality in society, people need to (1) have access to inequality cues in the world, (2) attend to these cues, (3) comprehend these cues, (4) process these cues (often succumbing to motivational biases), and (5) summarize these cues into a meaningful representation of inequality. Our framework highlights when and why lay people may misperceive the scope of inequality in society and provides a roadmap for research to examine how the processes in people's minds affect the outcomes researchers are ultimately interested in.
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 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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.007 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| 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".