GENDER EQUITY AGAINST “ECONOMIC REALITIES”: HOW A CONFLICT BETWEEN TWO MOVEMENTS RESHAPED THE CULTURAL UNDERSTANDING OF PAY*
Bibliographic record
Abstract
In the United States today, there is a broad cultural understanding that market forces drive pay outcomes. But prior to the 1980s, pay was understood to be the product of bureaucratic processes internal to organizations. The question of whether pay is determined by the market or organizational decisions is essential for evaluating employers’ liability for gender pay inequality, as employers are not responsible for inequalities resulting from the “economic realities” of the labor market. This article locates the shift in cultural beliefs about pay in key court decisions in the 1970s and 1980s. At that time, a social movement for pay equity used the idea of comparable worth to hold organizations accountable for inequality between jobs held by women and similarly valuable jobs held by men. But the judges who ruled on these cases were informed by a different movement, known as law and economics, which led them to conceptualize pay as the product of market forces instead of organizational decisions. These judges’ decisions limited employers’ liability for the pay gap and precipitated a transformation of the cultural common sense of pay within organizations, which increasingly adopted market-based approaches. The case of comparable worth highlights the role of judges, who have a unique role in determining the impact of social movements while themselves being targeted by such movements.
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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 | 0.029 | 0.027 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.022 | 0.107 |
| Scholarly communication | 0.020 | 0.015 |
| Open science | 0.002 | 0.017 |
| Research integrity | 0.010 | 0.016 |
| Insufficient payload (model declined to judge) | 0.005 | 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".