Advancing Underrepresented Populations in the Public Sector: Approaches and Practices in the Instructional Pipeline
Bibliographic record
Abstract
Although the numbers of women and minorities have steadily risen in the United States federal workforce, some studies have suggested that these groups are still underrepresented in high-level positions. Notwithstanding, surprisingly, only a few studies have examined the recruitment and achievement gap among disadvantaged groups in programs of public administration/policy/affairs with the aim of investigating their role as a pipeline to representation. This study is a step in that direction. It surveys academic heads of U. S. schools accredited by the Network of Schools of Public Policy, Affairs and Administration (NASPAA). The survey focuses on four key areas: academic support, financial support, recruitment strategies, and training and development. Among others, findings show for instance that schools with a lower percentage of students from underrepresented groups use scholarships, tuition waivers, and teaching assistantships to recruit students from these populations; in comparison, schools with higher percentages of students from underrepresented groups are able to attract faculty from minority groups at twice the rate of schools with lower percentages of students from underrepresented groups.
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.
How this classification was reachedexpand
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 | 0.005 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".