Social, Demographic, and Institutional Effects on African American Graduation Rates in U.S. Colleges and Universities.
Bibliographic record
Abstract
Improving the retention and graduation of African Americans and other minority groups in higher education is an important but highly politicized issue on college and university campuses. Prior studies emphasize the relationship between minority retention and achievement, cultural diversity, and racial policies and climates at predominantly White colleges and universities in the United States. In response to the need for further research, the effects of institutional actions related to diversity, minority group and African American retention, and social integration initiatives on African American graduation rates were examined for a national sample of United States (U.S.) colleges and universities. From a potential list of 7,018 colleges and universities, 2,233 met the inclusion criteria for the study. But necessary and complete information from national directories and the census could only be found for the final sample of 1,105. After dropping 30 outliers, several multiple regression analyses identified the institutional actions, social, and demographic factors that best predicted graduation rates. Public U.S. colleges and universities located in the Midwest region had lower African American graduation rates than private colleges and universities located in the South. Higher African American graduation rates occurred in colleges and universities with Black cultural centers, higher first-year retention rates, higher women enrollment rates, a higher number of student organizations, and Division III athletic programs. Colleges and universities located in a town had higher African American graduation rates than those located in a city, suburb, or rural area.
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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 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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 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".