The gender gap in attitudes toward workplace technological change
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
Abstract We provide the first systematic analysis of how attitudes toward workplace automation and artificial intelligence (AI) vary by gender, using survey data from ten countries. Our analyses reveal a significant gender gap in the perceived fairness of automation and AI, similar in magnitude to that of job offshoring. Drawing on the literature on economic shocks, we examine four explanations based on gender differences in (a) economic self-interest, (b) technological knowledge, (c) sociotropic concerns and (d) social status perceptions. Including these variables in our models, however, narrows the observed gender gap by only 40%. To better understand the sources of attitudinal variation by gender, we rely on Kitagawa–Oaxaca–Blinder decomposition, which shows that distributional differences in group characteristics, specifically women’s lower levels of technological knowledge and self-reported social status, account for approximately one-third of the gap, while the other two-thirds are explained by differences in how specific variables differentially influence attitudes.
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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) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.009 |
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