Gender Sorting and the Glass Ceiling in High-Tech Firms
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
With few exceptions, studies have conceived of the glass ceiling as reflecting internal promotion biases. In this article, the authors argue that glass ceiling patterns can also be the result of external recruitment and hiring processes. Using data on people applying by means of the Internet for jobs at 441 small- and medium-sized high-tech firms, they find evidence that the glass ceiling is produced by both internal and external hiring processes. On the supply side, females are sorted into lower-level job queues than males. On the demand side, screening biases against women also are evident, but a series of “what if” simulations suggest that demand-side screening processes play a comparatively minor role in producing the glass ceiling pattern. These results suggest that bias remediation policies designed to equalize gender differences in hiring chances are likely to be less effective than recruitment and outreach policies designed to improve gender disparities in candidate pools.
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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 | 0.001 |
| 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 | 0.000 |
| 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