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
Labor market outcomes depend, in part, upon an individual’s willingness to put him- or herself forward for different opportunities. We use a series of experiments to explore gender differences in willingness to apply for higher-return, more challenging work. We find that, in male-typed domains, qualified women are significantly less likely to apply than similarly well-qualified men. We provide evidence both in a controlled setting and in the field that reducing ambiguity surrounding required qualifications increases the rate at which qualified women apply. The effects are mixed for men. Our results point to a way to increase the pool of qualified women applicants. This paper was accepted by Yan Chen, behavioral economics and decision analysis. Funding: This work was funded by the National Science Foundation [Grant 1713752] and Harvard Business School. Supplemental Material: The e-companion and data are available at https://doi.org/10.1287/mnsc.2023.4907 .
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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.002 |
| Science and technology studies | 0.001 | 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) | 0.000 | 0.004 |
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