Training Professional Citizens: Getting beyond the Right Answer to the Wrong Question in Public Affairs Education
Bibliographic record
Abstract
Keynote address dettvered at NASPAA Annual Conference in Indianapolis, Indiana, on October 22, 2004. I am deeply honored by invitation to speak to you today, and also deeply gratefiil.This presentation has given me an opportunity to reflect on my long association with public affairs education, stretching back to my undergraduate years in Woodrow Wilson School at Princeton, my engagement in debates that led to formation of Kennedy School at Harvard, years I spent at Institute for Policy Sciences at Duke, my experiences in Office of Management and Budget, and finally, effort I made to fashion what we like to think is first of a second generation of policy programs at Johns Hopkins. I have titled my remarks Training Professional Citizens: Getting Beyond Right Answer to Wrong Question in Public Affairs Education. touchstone for my comments is extraordinary wave of governmental reform that has swept world over past two decades. From United States and Canada to Malaysia and New Zealand, governments are being reinvented, downsized, privatized, devolved, decentralized, deregulated, delayered, subjected to performance measurement, and contracted out, all in an effort to improve public sector performance.1 As United Nations Development Programme (1997, 1) noted in a report,uThe discourse on role of has moved to the center of international and national debate. The question is no longer how to shrink government report notes, how to improve governance. What better time, therefore, to be reflecting on structure and content of education needed to achieve this objective. My message to you today, however, may be a bit unsettling, for I am convinced that much of recent public sector reform movement has advanced right answer, but to wrong question. What is more, I believe that public affairs education bears a partperhaps a significant J-PAE 11 (2005): 1:7-1 9
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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 | 0.050 | 0.060 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.030 | 0.034 |
| Scholarly communication | 0.026 | 0.040 |
| Open science | 0.003 | 0.020 |
| Research integrity | 0.028 | 0.033 |
| Insufficient payload (model declined to judge) | 0.012 | 0.004 |
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".