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Record W2344232447

A dirty deed done dirt cheap: reporting the blame of a national reform on local politicians

2015· preprint· en· W2344232447 on OpenAlexaff
Aurélie Cassette, Étienne Farvaque

Bibliographic record

VenueMunich Personal RePEc Archive (Ludwig Maximilian University of Munich) · 2015
Typepreprint
Languageen
FieldSocial Sciences
TopicFrench Urban and Social Studies
Canadian institutionsCenter for Interuniversity Research and Analysis on Organizations
Fundersnot available
KeywordsDeedBlameBoycottAccountabilityPublic administrationGovernment (linguistics)Local governmentPolitical scienceBusinessPublic economicsEconomicsPoliticsLaw
DOInot available

Abstract

fetched live from OpenAlex

This paper tests the hypothesis that upper-level governments can transfer the accountability of the costs of a reform to a lower one. The reform of the school week in France provides the ground for a verification of the attribution of accountability hypothesis, as it was nationally decided and locally implemented, right before a municipal election. The results confirm that local incumbents have taken the blame of the reform, especially in larger cities. In this case, thus, the cost of the reform is borne twice by the lower level of government, financially and politically. So doing, the central government does a dirty deed to the local ones, for a very cheap cost. That mayors who have announced a boycott of the reform have received electoral gains confirms that some local politicians expected to be the fall guys, bearing the brunt of the costs of the reform.

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)0.014
metaresearch head score (Gemma)0.084
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.054
Threshold uncertainty score0.107

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0140.084
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0020.003
Science and technology studies0.0030.003
Scholarly communication0.0040.003
Open science0.0010.002
Research integrity0.0020.002
Insufficient payload (model declined to judge)0.0100.001

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 head0.086
GPT teacher head0.285
Teacher spread0.199 · 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 designNot applicable
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

Citations0
Published2015
Admission routes1
Has abstractyes

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