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Record W4224090155 · doi:10.1017/9781009128834.004

Beyond the Body Politic: Territory, Population and Colonial Projecting

2022· book-chapter· en· W4224090155 on OpenAlexaff
Ted McCormick

Bibliographic record

VenueCambridge University Press eBooks · 2022
Typebook-chapter
Languageen
FieldEconomics, Econometrics and Finance
TopicHistorical Economic and Social Studies
Canadian institutionsConcordia University
Fundersnot available
KeywordsEmpireColonialismBody politicPopulationPoliticsHistoryCorporate governanceState (computer science)Economic historyPolitical economyGenealogyPolitical scienceGeographySociologyLawAncient historyArchaeologyDemographyManagementEconomics

Abstract

fetched live from OpenAlex

Chapter 3 traces the expansion of demographic governance from ad hoc engagements with specific multitudes to a more systematic approach to the mobility and mutability of populations across expanding imperial territory. Important to this shift was the impact of reason-of-state political thought, notably in the work of Jean Bodin and Giovanni Botero, who both treated policy as an art that could improve upon or perfect nature. Botero drew attention to the instrumental use of colonies in managing population growth, and the chapter turns to English thinking about empire (in Richard Hakluyt’s Discourse of Western Planting and other works) as a solution to the threat of overpopulation – and to early colonial settlements in Virginia and New England as sites for envisioning the transplantation and transformation of excess or idle English people into loyal and industrious colonial subjects. Closing with a consideration of themes raised in Francis Bacon’s Essays or Counsels, Civil and Moral, the chapter argues that by the second quarter of the seventeenth century, demographic governance was seen as a matter of constant management of populations across England and its expanding empire.

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)0.000
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: none
Teacher disagreement score0.013
Threshold uncertainty score0.026

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0030.008
Scholarly communication0.0040.002
Open science0.0000.002
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0080.001

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 head0.027
GPT teacher head0.190
Teacher spread0.163 · 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
Published2022
Admission routes1
Has abstractyes

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