Creating an Accelerated Joint BA-MPA Degree Program for Adult Learners
Bibliographic record
Abstract
The increase in the number of adult learners in higher education has been dramatic in recent years. However, traditional MPA programs have done little to attract and accommodate these nontraditional students. This article discusses a collaborative effort between a school for professional studies offering B.A. degrees for adult learners and a department of public policy studies offering a traditional MPA degree to create an accelerated BA-MPA degree. The accelerated degree program was designed to address the need for the undergraduate program to expand its program offerings to include graduate degrees in order to tap an unfilled market. The graduate program wanted to address the need to bolster its enrollment and to increase the diversity of its student body. The article discusses several factors that have contributed to the early success of the accelerated BA-MPA program, including institutional merger, leadership support, market need, internal support, adult student support mechanisms, streamlined application process, and program liaisons. The effort to create a new program and implement it has not been without challenges, however. Chief among these have been cultural and programmatic differences between the collaborating department and the school, including traditional versus nontraditional student populations, undergraduate versus graduate education, a business versus public and nonprofit orientation, and a quarter versus semester format.
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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 | 0.005 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.010 | 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".