MétaCan
Menu
Back to cohort
Record W2275603374 · doi:10.1017/s1755773915000351

Institutions and attribution of responsibility outside the electoral context: a look at French semi-presidentialism

2015· article· en· W2275603374 on OpenAlexaff
Mathieu Turgeon, Éric Bélanger

Bibliographic record

VenueEuropean Political Science Review · 2015
Typearticle
Languageen
FieldSocial Sciences
TopicPolitical Theory and Influence
Canadian institutionsMcGill University
Fundersnot available
KeywordsPresidential systemPoliticsPolitical scienceContext (archaeology)Political economyCLARITYDemocracyAttributionPublic administrationSociologyLawSocial psychology

Abstract

fetched live from OpenAlex

Some institutional arrangements may be undesirable for democracy by obscuring which political actors are to be held responsible for failed or successful policies and bad or good macroeconomic performances. Much of the work in the area has focused on whether institutions affect the ‘clarity of political responsibility’ and the ability of voters to punish or reward, in turn, governments and elected officials. Not much has been said, however, about the assignment of responsibility outside the electoral context, for a broad range of policy areas. This paper explores these questions in the context of French semi-presidentialism. It demonstrates that the French public is surprisingly quite responsive to the demands imposed by their political system by adjusting reasonably well their evaluations of both actors of the executive in light of major political events and changes in the economic conditions when the circumstances clearly indicate which of the two is ‘in charge’. At other times, however, this particular institutional arrangement obscures instead political responsibility.

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)0.005
metaresearch head score (Gemma)0.005
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: Empirical · Consensus signal: Empirical
Teacher disagreement score0.040
Threshold uncertainty score0.079

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0050.005
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0030.002
Science and technology studies0.0020.007
Scholarly communication0.0050.002
Open science0.0000.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0020.000

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 head0.113
GPT teacher head0.402
Teacher spread0.289 · 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
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

Citations7
Published2015
Admission routes1
Has abstractyes

Explore more

Same venueEuropean Political Science ReviewSame topicPolitical Theory and InfluenceFrench-language works237,207