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Record W2133979406 · doi:10.4236/ojps.2013.32010

Quinquennial Terror: Machiavelli’s Understanding of the Political Sublime

2013· article· en· W2133979406 on OpenAlexaff
Ed King

Bibliographic record

VenueOpen Journal of Political Science · 2013
Typearticle
Languageen
FieldArts and Humanities
TopicRenaissance and Early Modern Studies
Canadian institutionsConcordia University
Fundersnot available
KeywordsSublimeAdmirationPoliticsGeniusVerisimilitudeCrueltyReignHonorFanaticismRulerAestheticsPhilosophyLawLiteratureArtSociologyPolitical science

Abstract

fetched live from OpenAlex

This paper argues that far from advocating fear of violence as a continuous source of civic provocation Machiavelli’s ideal ruler employs an aesthetic approach to civic violence; one that actually harms few citizens and moderates their fear with admiration through carefully considered psychological imperatives similar to those articulated two hundred years later in theories of the sublime. Such violence as there was would occur half a decade at a time in between which the citizens and the patria would enjoy stability, wealth and honor. It had a proven Medici provenance, having been developed through Cosimo de Medici’s intuitive genius for governance and was maintained by Piero and Lorenzo the Magnificent. The insight was empirically confirmed by Niccolò’s observations of similarly intuitive political savants; namely Cesare Borgia and Julius II. It was not given a technical title by Machiavelli, who unhelpfully referred to it as crudeltà bene usate (cruelty well used) but we might call it “the politics of the sublime”. Despite its most dramatic (and consequentially disproportionate) evocation in the Prince, Machiavelli’s reliance on the political sublime waned throughout his literary career, until he rejected it in a stunning critique of Cosimo’s reign in the Florentine Histories.

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 categoriesScience and technology studies, Insufficient payload (model declined to judge)
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 score0.391
Threshold uncertainty score0.999

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 studies0.0010.004
Scholarly communication0.0010.001
Open science0.0010.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.135
GPT teacher head0.315
Teacher spread0.180 · 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.

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
Published2013
Admission routes1
Has abstractyes

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