Toward a more public discussion of the ethics of federal social program evaluation
Bibliographic record
Abstract
Abstract Federal social program evaluation has blossomed over the past quarter century. Despite this growth, there has been little accompanying public debate on research ethics. This essay explores the origins and the implications of this relative silence on ethical matters. It reviews the federal regulations that generally govern research ethics, and recounts the history whereby the evaluation of federal programs was specifically exempted from the purview of those regulations. Through a discussion of a recent evaluation that raised ethical concerns, the essay poses—but does not answer—three questions: (1) Are there good reasons to hold federal social program evaluations to different standards than those that apply to other research?; (2) If so, what ethical standards should be used to assess such evaluations?; and (3) Should a formal mechanism be developed to ensure that federal social program evaluations are conducted ethically? © 2005 by the Association for Public Policy Analysis and Management
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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 | 0.308 | 0.287 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.017 | 0.080 |
| Scholarly communication | 0.035 | 0.031 |
| Open science | 0.005 | 0.011 |
| Research integrity | 0.044 | 0.048 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".