Educating for uncertainty: Integrating abductive reasoning into the public policy curriculum
Bibliographic record
Abstract
Traditional quantitative policy analysis often fails to effectively address complex, uncertain, and value-laden policy challenges due to limitations rooted in data unavailability, causal complexity, and difficulty in modeling value conflicts. This paper argues that, in practice, policymakers and advisors compensate for these shortfalls by employing abductive reasoning, drawing upon their experience and knowledge to fill analytical gaps. Abductive reasoning emphasizes the common policy tasks of generating plausible hypotheses from limited evidence, engaging in experimentation, and adapting to emergent outcomes. Considering its central, albeit often unrecognized, role in policymaking, the paper contends that abductive reasoning should be formally integrated into policy analysis education, where it is currently ignored or underestimated. Although many policy analysts implicitly employ abductive logic, formally teaching it and cultivating appropriate tools and mindsets can help deploy it more systematically, providing a more versatile and realistic approach to problem-solving than existing methods that rely on deductive or inductive techniques. Integrating abduction into policy curricula faces challenges, such as the risks of bias and misuse, which must be mitigated through transparency and intellectual humility. Nonetheless, the complexity of contemporary policy problems demands rethinking policy analysis education and reorienting it toward enhancing abductive reasoning skills.
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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 | 0.060 | 0.088 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.002 | 0.010 |
| Scholarly communication | 0.010 | 0.012 |
| Open science | 0.004 | 0.009 |
| Research integrity | 0.003 | 0.008 |
| Insufficient payload (model declined to judge) | 0.004 | 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".