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Record W4385616827 · doi:10.1177/14789299231191431

Ideas, Policy Feedback and the American Political Economy

2023· article· en· W4385616827 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

VenuePolitical Studies Review · 2023
Typearticle
Languageen
FieldSocial Sciences
TopicSocial Policy and Reform Studies
Canadian institutionsMcGill University
Fundersnot available
KeywordsPoliticsInternational political economyAmerican political scienceCurrencyPolitical economyPolitical sciencePublic policyPolitics of the United StatesSociologyEconomicsLaw

Abstract

fetched live from OpenAlex

In their volume The American Political Economy, Jacob S. Hacker et al. seek to renew the study of American political economy (APE) through a direct engagement with other areas of political science, including and especially comparative political economy (CPE). In the introduction of their volume, they lay out the foundations of APE as both a field of research and an approach to American politics that seeks to contribute to the study of the United States as well as to the broader discipline of political science. In this review essay, I will discuss the APE as an intellectual project to stress its key assumptions and its potential contribution to the study of politics and public policy, in the United States and beyond. Then, I will discuss two key issues that, while not explicitly central to APE as developed in The American Political Economy, could help enrich this novel approach. These two issues are policy feedback, a concept already prominent in the institutionalist tradition APE draws on, and the role of ideas in politics, which in recent decades has gained more currency in the study of politics, public policy and CPE.

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)0.001
metaresearch head score (Gemma)0.006
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesScience and technology studies
Consensus categoriesScience and technology studies
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: Theoretical or conceptual
GenreCandidate signal: Commentary · Consensus signal: none
Teacher disagreement score0.517
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.006
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.001
Science and technology studies0.0010.009
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.084
GPT teacher head0.451
Teacher spread0.367 · 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