Motivated Reasoning and Risk Governance: What Risk Scholars and Practitioners Need to Know
Bibliographic record
Abstract
Abstract Empirical research in psychology and political science shows that individuals collect, process, and interpret information in a goal-driven fashion. Several theorists have argued that rather than striving for accuracy in their conclusions, individuals are motivated to arrive at conclusions that align with their previous beliefs, values, or identity commitments. The literature refers to this phenomenon broadly as ‘motivated reasoning’. In the context of risk governance, motivated reasoning can help to explain why people vary in their risk perceptions, evaluations, and preferences about risk management. But our current understanding of the phenomenon is incomplete, including the degree to which motivated reasoning should be considered rational and reasonable. Further, the research on motivated reasoning is largely unknown among risk practitioners. This chapter identifies key theoretical models of motivated reasoning, discusses the conceptual differences between them, and explores the implications of motivated reasoning for risk governance. Motivated reasoning is often labeled as ‘irrational’ and thus seen to prevent effective decision-making about risk, but this chapter challenges this assessment. The chapter concludes by identifying theoretical and empirical implications for researchers studying motivated reasoning and risk, as well as practical implications for policymakers and regulators involved in risk governance.
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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 | 0.010 | 0.014 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.004 | 0.022 |
| Scholarly communication | 0.015 | 0.023 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.008 | 0.012 |
| Insufficient payload (model declined to judge) | 0.007 | 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".