Acceptable Input: Using Decision Analysis to Guide Public Policy Deliberations
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Multiparty deliberative processes have become a popular way to increase public participation in public policy choices. Their legitimacy depends on participants' ability, first, to understand the issues facing them and, then, to form and express their own positions on them. These tasks pose significant cognitive and emotional challenges. This paper argues that decision analysis, informed by behavioral decision research, offers procedures and standards for creating responsible deliberative processes. These involve (a) formal analysis of decisions, identifying the kernel of most relevant information, (b) communication procedures, recognizing the strengths and weaknesses of lay understanding, and (c) interactive elicitation methods, helping individuals to articulate the implications of their values for specific settings. A construct validity criterion assesses the extent to which the resulting valuations are properly sensitive to decision features. Feasible extensions of traditional decision analysis create opportunities to formalize the aspirations of participants and ensure that the intellectual content of deliberative processes is worthy of the political hopes vested in them.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.007 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.012 | 0.005 |
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 it