The White standard: Racial bias in leader categorization.
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
In 4 experiments, the authors investigated whether race is perceived to be part of the business leader prototype and, if so, whether it could explain differences in evaluations of White and non-White leaders. The first 2 studies revealed that "being White" is perceived to be an attribute of the business leader prototype, where participants assumed that business leaders more than nonleaders were White, and this inference occurred regardless of base rates about the organization's racial composition (Study 1), the racial composition of organizational roles, the business industry, and the types of racial minority groups in the organization (Study 2). The final 2 studies revealed that a leader categorization explanation could best account for differences in White and non-White leader evaluations, where White targets were evaluated as more effective leaders (Study 3) and as having more leadership potential (Study 4), but only when the leader had recently been given credit for organizational success, consistent with the prediction that leader prototypes are more likely to be used when they confirm and reinforce individualized information about a leader's performance. The results demonstrate a connection between leader race and leadership categorization.
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 | 0.002 | 0.000 |
| 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 | 0.000 |
| 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