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Record W2078573311 · doi:10.3138/ecf.26.4.565

Between Excess and Inanition: Tobias Smollett’s Medical Model of the State

2014· article· en· W2078573311 on OpenAlexvenueno aff
Douglas Duhaime

Bibliographic record

VenueEighteenth-Century Fiction · 2014
Typearticle
Languageen
FieldEconomics, Econometrics and Finance
TopicHistorical Economic and Social Studies
Canadian institutionsnot available
Fundersnot available
KeywordsScholarshipReactionaryPoliticsBody politicExtant taxonMetaphorReading (process)Political economySociologyPolitical sciencePhilosophyLawTheology

Abstract

fetched live from OpenAlex

Tobias Smollett’s medical training in the Boerhaavian tradition helped shape his contributions to debates on luxury, British foreign policy, and public economics. He also invested his medical philosophy with a vast range of political import. This article draws on recent scholarship to outline some of the ways in which medical thought informed the political sensibilities of those writing before Smollett, from Gerard de Malynes and Edward Misselden to William Petty and François Quesnay. Reading Smollett’s novels vis-à-vis his medical and historical works, I analyze the ways in which Smollett deployed his medical philosophy to naturalize his reactionary agenda on issues from Anglo-Scottish fiscal policy to the Seven Years’ War. Attending to Smollett’s revision of the body politic metaphor can help resolve extant scholarly debates concerning Smollett’s axiological orientation.

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)0.002
metaresearch head score (Gemma)0.002
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: none
Teacher disagreement score0.014
Threshold uncertainty score0.047

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0040.026
Scholarly communication0.0050.004
Open science0.0010.002
Research integrity0.0030.004
Insufficient payload (model declined to judge)0.0040.001

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 head0.031
GPT teacher head0.201
Teacher spread0.171 · 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

Citations2
Published2014
Admission routes1
Has abstractyes

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