Increasing underrepresented minority participation in the STEM PhD programs
Bibliographic record
Abstract
According to 2014 data, underrepresented minorities (URM) made up 36.8% of America?s college age (18-24) population (NSF, 2017b), but only received 12.2% of PhD degrees (NSF, 2017a) and a scant 7.9% of all PhD degrees in the science and engineering fields (NSF, 2017a). The figures mean that URM received only a quarter of the science and engineering PhD degrees relative to their percentage makeup of America?s college age population. At UCSF though, the percentage of URM enrolled in UCSF?s basic and biomedical sciences PhD programs is 22% (UCSF Graduate Division, 2020a). UCSF was chosen for this evaluation study because the percentage of URM students enrolled in its basic and biomedical sciences PhD programs is nearly three times the national average and UCSF is classified as a ?very high research activity? or R1 institution by the Carnegie Classification (Carnegie Classification of Institutions of Higher Education by Indiana University Center for Postsecondary Research, 2017). R1 institutions award the most science and engineering doctorate degrees (London et al., 2014). The study will utilize the Clark and Estes? (2008) gap analysis framework to evaluate the knowledge, motivation, and organizational (KMO) influences from the student perspective. The research methodology uses a quantitative approach consisting of an online survey sent to URM students enrolled in the basic and biomedical sciences PhD programs. The study concludes with a series of recommendations to help UCSF ensure its continued success with a more diverse student body population in the basic and biomedical sciences PhD programs.
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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 | 0.003 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".