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Record W3201777684 · doi:10.33137/rr.v44i2.37522

Plague Time: Space, Fear, and Emergency Statecraft in Early Modern Italy

2021· article· en· W3201777684 on OpenAlexvenueno aff
Nicholas A. Eckstein

Bibliographic record

VenueRenaissance and Reformation · 2021
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicYersinia bacterium, plague, ectoparasites research
Canadian institutionsnot available
Fundersnot available
KeywordsPlague (disease)AuthoritarianismExistentialismDimension (graph theory)Michel foucaultHistoryEarly modern EuropePoliticsPolitical economySociologyEconomic historyAncient historyPolitical scienceLawDemocracy

Abstract

fetched live from OpenAlex

Michel Foucault argued famously that early modern European governors responded to plague by quarantining entire urban populations and placing citizens under minute surveillance. For Foucault, such sixteenth- and seventeenth-century policies were the first steps towards an authoritarian paradigm that would only emerge in full in the eighteenth century. The present article argues that Foucault’s model is too abstracted to function as a tool for the historical examination of specific emergencies, and it proposes an alternative analytical framework. Addressing itself to actual events in early modern Italy, the article reveals that when plague threatened, Florentine and Bolognese health officials projected themselves into a spatio-temporal dimension in which official actions and perceptions were determined solely by the spread of contagion. This dimension, “plague time,” was not a stage on the irresistible journey towards Foucault’s “utopia of the perfectly governed city.” A contingent response to a recurrent existential menace, plague time rose and fell in response to events, and may be understood as a season.

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)0.001
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 score0.009
Threshold uncertainty score0.055

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0030.019
Scholarly communication0.0040.003
Open science0.0000.003
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 head0.008
GPT teacher head0.255
Teacher spread0.246 · 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
Published2021
Admission routes1
Has abstractyes

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