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Record W4388813142 · doi:10.1080/17457289.2023.2281388

A cruise-and-crash model of the cost of ruling

2023· article· en· W4388813142 on OpenAlexaboutno aff
Alfred G. Cuzán, Richard J. Heggen

Bibliographic record

VenueJournal of Elections Public Opinion and Parties · 2023
Typearticle
Languageen
FieldSocial Sciences
TopicElectoral Systems and Political Participation
Canadian institutionsnot available
Fundersnot available
KeywordsSpellPoliticsDemocracyState (computer science)Law and economicsPolitical scienceLawEconomicsSociologyMathematics

Abstract

fetched live from OpenAlex

Since the publication of Nannestad and Paldam ([1999]. “The Cost of Ruling. A Foundation Stone for Two Theories,” University of Aarhus, Denmark. Working Paper No. 1999-9. http://www.martin.paldam.dk/Papers/Gamle/Cost-of-ruling.PDF.) on the cost of ruling, their finding that incumbents lose votes in the order of 2 to 3 percentage points per term has gained the status of something like an “inductive law” of politics (Budge [2019]. Politics. A Unified Introduction to How Democracy Works. Abingdon/Oxon/New York: Routledge, 2019). We suggest that this generalization conjures up an inaccurate image, that of a gradual reduction of a ruling party’s vote over the course of an incumbency or “spell.” Our own analyses of national and state or provincial elections in Australia, Canada, Germany, and the United States demonstrate that this interpretation is not correct. Instead, while they remain in office the ruling party cruises along, averaging more or less the same share of the vote election after election, until they fail to win another term, at which point all or nearly all of the cost of ruling, like a balloon payment on a loan, comes due. Moreover, on average the magnitude of the loss is independent of the length of their stay in office. This means that a long incumbency confounds the expectations fostered by the “inductive law” because the overall loss – which we find to be reasonably constant across a wide spectrum of data sets – divided by a spell length exponentially distributed from one to many terms cannot be a constant. Nannestad and Paldam's assertion of 2.25 percentage points per term vote loss is at best a description of a mid-range value.

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 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)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.524
Threshold uncertainty score0.193

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
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 head0.179
GPT teacher head0.395
Teacher spread0.216 · 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 teacher head, 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

Citations2
Published2023
Admission routes1
Has abstractyes

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