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Record W4417271003 · doi:10.1080/09692290.2025.2586602

Endogenizing the limits of ideas: a ‘how-to’ guide to understanding ignorance and failure in ideational political economy

2025· article· en· W4417271003 on OpenAlexafffund
Jacqueline Best

Bibliographic record

VenueReview of International Political Economy · 2025
Typearticle
Languageen
FieldSocial Sciences
TopicPolitical Economy and Marxism
Canadian institutionsUniversity of Ottawa
FundersSocial Sciences and Humanities Research Council of CanadaCopenhagen Business School
KeywordsIgnoranceInternational political economyPoliticsGlobalizationNew political economy

Abstract

fetched live from OpenAlex

This commentary essay makes the case for a different way of conceptualizing the role of ideas in political economy by arguing for the need to endogenize the limits of ideas—recognizing the key roles played by ignorance and failure in economic policymaking. The central role played by the limits of ideas poses a challenge for much ideational political economy scholarship, which has not yet adequately recognized their place. This challenge is particularly acute for classical ideational political economy—the scholarship most directly descended from Peter Hall’s (Citation1993) work on policy paradigms and social learning. Yet the recent ‘practice turn’ in political economic scholarship also has its own blind spots regarding the limits of ideas, in large measure because it continues to assume that failure and ignorance are temporary problems that are generally resolved through social learning. Instead, I show that ignorance and failure are not only widespread and often persistent, but that they can also be functional to economic policymaking: a feature rather than a bug in political economic theory and practice.

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.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.020
metaresearch head score (Gemma)0.030
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: Theoretical or conceptual
GenreCandidate signal: Methods · Consensus signal: none
Teacher disagreement score0.020
Threshold uncertainty score0.104

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0200.030
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0060.004
Science and technology studies0.0060.083
Scholarly communication0.0160.028
Open science0.0050.007
Research integrity0.0100.019
Insufficient payload (model declined to judge)0.0040.001

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 head0.044
GPT teacher head0.361
Teacher spread0.317 · 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

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designTheoretical or conceptual
Domainnot available
GenreMethods

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".

Quick stats

Citations0
Published2025
Admission routes2
Has abstractyes

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