Statistical Discrimination and the Rationalization of Stereotypes
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
The theory of statistical discrimination is a dominant social scientific framework for understanding discrimination in labor markets. To date, the literature has treated this theory as a model that merely explains employer behavior. This article contends that the idea of statistical discrimination, rather than simply providing an explanation, can lead people to view social stereotyping as useful and acceptable and thus help rationalize and justify discriminatory decisions. A preregistered survey experiment with more than 2,000 participants who had managerial experience shows that exposure to statistical discrimination theory strengthened people’s belief in the accuracy of stereotypes, their acceptance of stereotyping, and the extent to which they engaged in gender discrimination in a hiring simulation. Reading a critical commentary on the theory mitigated these effects. These findings imply that theories of discrimination, and the language associated with them, can rationalize—or challenge the rationality of—stereotypes and discrimination and, as a result, shape the attitudes and actions of decision-makers in labor markets.
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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.003 |
| 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