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Punctuated Equilibrium in Comparative Perspective

2009· article· en· W2169495003 on OpenAlexaff
Frank R. Baumgartner, Christian Breunig, Christoffer Green‐Pedersen, Bryan D. Jones, Peter Bjerre Mortensen, Michiel Nuytemans, Stefaan Walgrave

Bibliographic record

VenueAmerican Journal of Political Science · 2009
Typearticle
Languageen
FieldSocial Sciences
TopicElectoral Systems and Political Participation
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsPunctuated equilibriumKurtosisDistribution (mathematics)Government (linguistics)PoliticsPerspective (graphical)Control (management)Public economicsEconomicsPolitical scienceEconomic systemLawStatisticsBiologyMathematics

Abstract

fetched live from OpenAlex

We explore the impact of institutional design on the distribution of changes in outputs of governmental processes in the United States, Belgium, and Denmark. Using comprehensive indicators of governmental actions over several decades, we show that in each country the level of institutional friction increases as we look at processes further along the policy cycle. Assessing multiple policymaking institutions in each country allows us to control for the nature of the policy inputs, as all the institutions we consider cover the full range of social and political issues in the country. We find that all distributions exhibit high kurtosis values, significantly higher than the Normal distribution which would be expected if changes in government attention and activities were proportionate to changes in social inputs. Further, in each country, those institutions that impose higher decision‐making costs show progressively higher kurtosis values. The results suggest general patterns that we hypothesize to be related to boundedly rational behavior in a complex social environment.

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)0.002
metaresearch head score (Gemma)0.008
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.014
Threshold uncertainty score0.048

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.008
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0020.002
Science and technology studies0.0010.002
Scholarly communication0.0020.003
Open science0.0010.002
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0140.001

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 head0.053
GPT teacher head0.426
Teacher spread0.374 · 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 designObservational
Domainnot available
GenreEmpirical

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

Citations498
Published2009
Admission routes1
Has abstractyes

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