DoctorateDegrees in Mathematics Earnedby Blacks His1]uO23+3CusC and NativeAmericans
Bibliographic record
Abstract
mber of total degrees Oneans+[2 theunderrepresC1+uss ques(+C pos( in the introduction by comparing the percentages in Table 1 with the current ethnic makeup of the U.S. population. ForanalysC) we als compare the data in Table 1 with the projected demographics of the U.S. population in the year 2025 [9], [10]. Thes comparis2u are contained in Table 2. The table sble that theunderrepres))Cu tionis snu+I1(3uO) Although BHNs represent one quarter of the U.S. population, they haveearned less than 5% of thedecO"ExE dcO"E in the mathematical sciences. Itals ss( that if currenttrends continue, theunderrepres+(1usu will become much wors forHis3]CCuO])1Cus as they continue to increas theirrepres)C3uO[] in the total U.S. population. Herbert A.Medk; is professor of mathematics at Loyola Marymount University. His emailadilcF is hmedina@l65 22 The AMS publishes its dsc on mathematicsdathemat d gree recipientsbased onacadxWF yearsinstead ofcalendE years. Its 2002--03 preliminarydre is available
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.090 | 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 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".