Does Holding Beliefs with Conviction Prevent Policy Actors from Adopting a Compromising Attitude?
Bibliographic record
Abstract
Much of the political science literature argues that commitment to beliefs renders the attitudes of policy actors inflexible. Belief commitment encourages alliances among actors who think alike and creates a distance with those whose beliefs differ. As it cuts the flow of information between disagreeing actors, belief commitment constrains the attitudes of actors to consistency across a variety of objects and over time, preventing policy compromises. This article examines the possibility that different beliefs have different effects on attitude. Specifically, it hypothesizes that actors holding purposive beliefs have more consistent attitudes than actors holding material beliefs. Thanks to a survey of North American and European biotechnology policy actors, conducted twice between 2006 and 2008, it is shown that a strong commitment to purposive beliefs encourages attitude consistency across objects and over time, while equal commitment to material beliefs enables more attitude flexibility. Implications for democracy and policy-making compromises are discussed.
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How this classification was reachedexpand
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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 teacher head, 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".