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Record W2110057417 · doi:10.1080/13501760600924191

Punctuated equilibrium in French budgeting processes

2006· article· en· W2110057417 on OpenAlexaff
Frank R. Baumgartner, Martial Foucault, Abel François

Bibliographic record

VenueJournal of European Public Policy · 2006
Typearticle
Languageen
FieldEconomics, Econometrics and Finance
TopicFiscal Policies and Political Economy
Canadian institutionsCollège Lionel Groulx
Fundersnot available
KeywordsIncrementalismPunctuated equilibriumState (computer science)ParliamentEconomicsNeutralityLaw and economicsPolitical scienceLawPoliticsComputer science

Abstract

fetched live from OpenAlex

We use data on French budgeting to test models of friction, incrementalism and punctuated equilibrium. Data include the overall state budget since 1820; ministerial budgets for seven ministries since 1868; and a more complete ministerial series covering ten ministries since 1947. Our results in every case are remarkably similar to the highly leptokurtic distributions that Jones and Baumgartner (2005) demonstrated in US budgeting processes. This suggests that general characteristics of administrative processes create friction, and that these general factors are more important than particular details of organizational design. The legendary centralization and administrative strength of the French state, especially when compared to the decentralized separated powers structure of the US system, where the theory was developed, is apparently not sufficient to overcome cognitive pressures causing friction. Further, our French data cover a wide range of institutional procedures and constitutional regimes. The similarity of our findings across all these settings suggests that administrative structures alone are less important than the cognitive reasons discussed in the original model.

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)0.008
metaresearch head score (Gemma)0.033
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 score0.039
Threshold uncertainty score0.077

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0080.033
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0030.003
Science and technology studies0.0010.004
Scholarly communication0.0040.003
Open science0.0010.002
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0050.000

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 head0.026
GPT teacher head0.222
Teacher spread0.196 · 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

Citations93
Published2006
Admission routes1
Has abstractyes

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