A Model and Test of Policymaking as Process
Bibliographic record
Abstract
The prior policymaking literature has been largely theoretical and focused upon agenda setting and the initiation mechanisms of policy change, namely triggering events. Notably absent from the prior literature have been studies aimed at developing and empirically evaluating models of policymaking as a process with outcomes. Such a process/outcome model of policymaking would necessarily include initiation mechanisms, or triggering events, agenda setting, politics, and subsequent outcomes (intended vs. unintended). This paper applies such a process and outcome model through an evaluative case study of the tragic death of Martin Lee Anderson in a Florida juvenile boot camp. The death of Martin Lee Anderson in January, 2006 sparked a major debate and a series of subsequent policy initiatives in Florida related to juvenile boot camps, the treatment of juveniles in confinement, and overall accountability of the state’s juvenile justice system. Employing multiple data sources, the evaluation assesses the triggering events, subsequent processes, and resulting policy outcomes. The findings demonstrate that policymaking occurs in a sequential manner with identifiable stages. Different actors influence each stage of the process and help to shape the final policy outcomes. However, using the methods of in-depth interviews and participant observations revealed that outcomes are also shaped by various political negotiations which are not easily captured through conventional research and evaluation methods.
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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 | 0.008 | 0.022 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.009 |
| Scholarly communication | 0.006 | 0.008 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.004 | 0.003 |
| Insufficient payload (model declined to judge) | 0.021 | 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".