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Record W2063312367 · doi:10.1177/0010414008325283

How Do Ideas Matter?

2008· article· en· W2063312367 on OpenAlex

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.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueComparative Political Studies · 2008
Typearticle
Languageen
FieldSocial Sciences
TopicElectoral Systems and Political Participation
Canadian institutionsUniversity of British Columbia
Fundersnot available
KeywordsPoliticsArgument (complex analysis)GermanPositive economicsCognitionMechanism (biology)Affect (linguistics)Causal modelSocial psychologyEpistemologySociologyPsychologyPolitical scienceEconomicsLaw

Abstract

fetched live from OpenAlex

How do ideas affect political decision making? Despite much evidence that ideas matter, relatively little is known about the specific mechanisms through which they influence actors' beliefs, goals, and preferences. Drawing on psychological findings, the article elaborates a cognitive mechanism through which ideational frameworks shape political elites' preferences among options. It argues that actors' mental models of the domains in which they are operating systematically guide their attention within processes of decision making. By leading them to reason about certain causal possibilities and data and to ignore and discount others, politicians' and policy makers' mental models can powerfully shape their causal belief sets and, in turn, their policy preferences. Furthermore, these attentional effects help explain why ideas persist under some conditions but change under others. The argument is empirically probed through an examination of key episodes in German pension politics over seven decades, drawing on detailed records of high-level policy deliberations.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

Full frame distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.592
Threshold uncertainty score0.743

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.001
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.001

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.

Opus teacher head0.314
GPT teacher head0.464
Teacher spread0.149 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it