What Makes a Decision Fair? Relative Earnings, Gender, and Justifications for Couples’ Decision-Making
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
This article builds on research demonstrating that inequality is widely accepted when it results from practices that are perceived to be fair. Using a survey experiment on a nationally representative sample of US adults (n=3,978), the study adds new insight into the mechanisms that sustain gender inequality in relationships. Findings show that Americans’ beliefs about gender are relied on more often than economic explanations to diminish concerns about unfairness in decision-making. Respondents were more likely to view decisions as fair when made by women, even though respondents often drew on seemingly gender-neutral allocation rules to justify decision-making. Topic modeling of open-ended explanations also exposed how beliefs about gender are incorporated into fairness perceptions in ways that sustain men’s authority. The authors argue that the empirical patterns underpinning subjective perceptions of fairness are fundamental to understanding the persistence of inequality in gendered divisions of cognitive, emotional, and domestic labor.
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.
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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.002 |
| 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