Bibliographic record
Abstract
In the early 1990s the NYU/Wagner School undertook an effort to reform the curriculum of one of the oldest and largest public administration programs in the nation to make its graduates more job ready and better prepared to be “reflective practitioners.” While the reform applied to all parts of the School, the design of the public policy analysis specialization at Wagner School embraces all the elements of the “Clinical Initiative.” The curriculum was revised to provide the three “Es”: Exploration, Exercise and Experience. The crowning feature of the reform brought by the Clinical Initiative is the year-long, team capstone project, which replaces the comprehensive examination and the master’s thesis in the MPA program. Instead of taking an examination in the specialization, students now enroll in a two-semester course organized around student-team policy projects involving either consulting for public and nonprofit organization clients, or policy research studies. The projects integrate the knowledge, understanding and skills covered in public policy courses, and test the ability of students to meet the challenges of working in teams. The presentation of the Wagner Clinical Initiative pays special attention to the international capstone course, taught by the author, that often involves fieldwork abroad and teams of Wagner students working with students in partner universities in developing countries. In addition to providing effective policy analysis training, Wagner capstones constitute a significant public service.
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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 | 0.017 | 0.032 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.005 | 0.016 |
| Scholarly communication | 0.013 | 0.008 |
| Open science | 0.002 | 0.009 |
| Research integrity | 0.005 | 0.007 |
| Insufficient payload (model declined to judge) | 0.021 | 0.010 |
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".