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Record W2189064438

Leviathan governments and public debt

2008· article· en· W2189064438 on OpenAlexaff
Eric Stephens

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEconomics, Econometrics and Finance
TopicFiscal Policies and Political Economy
Canadian institutionsQueen's University
Fundersnot available
KeywordsDebtEconomicsCasualPosition (finance)Value (mathematics)Asset (computer security)PoliticsAgency (philosophy)ScarcityPublic economicsControl (management)Political scienceMicroeconomicsMacroeconomicsFinanceSociologyLaw
DOInot available

Abstract

fetched live from OpenAlex

Casual observation suggests that governments are not as willing to save as they are to spend. The paper analyzes the dynamics of public spending, taxation and debt in a political agency model. Policy choices are made by short-lived politicians who can be only partially controlled through the electoral process. The main focus of the paper is to consider the impacts of binding limits on the public budget. A unique budget restriction that optimizes citizen welfare is shown to exist. The model characterizes the trade ofi between the beneflts of better politician control and the cost that such restrictions will have in times of scarcity. The value of a limit depends both on the extent to which politicians goals deviate from their constituencies and how efiectively the electoral process disciplines them when they misbehave. The results also suggest that the value of such a restriction depends on the flscal position at the time in which it is imposed. In particular, the value of constraining politicians depends positively on the size of current public asset holdings.

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)0.001
metaresearch head score (Gemma)0.004
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 score0.010
Threshold uncertainty score0.035

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.004
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0010.002
Scholarly communication0.0070.003
Open science0.0000.002
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0070.000

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 head0.049
GPT teacher head0.201
Teacher spread0.152 · 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

Citations0
Published2008
Admission routes1
Has abstractyes

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