Gender Differences in First Jobs for New US PhDs in the Mathematical\n Sciences
Bibliographic record
Abstract
We take a long term look at initial employment trends for new doctorates with\nan eye towards gender, citizenship, and gender and citizenship differences by\nanalyzing data from 1991-2015 AMS-ASA-IMS-MAA- SIAM Annual Surveys. The data\nshow that the unemployment rate for women has been equal to or lower than the\nrate for men during most of the last quarter century. The one exception is that\nbetween 2001 and 2015 the unemployment rate for women who are not U.S. citizens\nwas higher than the rate for non-citizen men. The unemployment rates are higher\nfor males who are U.S. citizens than for non-citizen males in the last fifteen\nyears, a puzzling trend. The data show that men from all pure math programs are\nconsiderably more likely than women to take jobs at the top-ranking and\ntop-producing math departments. The data show women take jobs at departments in\nwhich the highest degree is a bachelor's degree at much higher rates and men\ntake jobs in business and industry at considerably higher rates. We also find\nthat men from the top-ranking or top-producing doctoral programs tend to be\nmore likely to take jobs at academic institutions or research institutes at\nleast on a par with their degreegranting institutions.\n
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.018 | 0.002 |
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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".