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Record W2317973034 · doi:10.1177/0843871416630268

Neither private contractors nor productive partners: The English fiscal-naval state and London hospitals, 1660–1715

2016· article· en· W2317973034 on OpenAlexafffund
Matthew Neufeld

Bibliographic record

VenueInternational Journal of Maritime History · 2016
Typearticle
Languageen
FieldEconomics, Econometrics and Finance
TopicHistorical Economic and Social Studies
Canadian institutionsUniversity of Saskatchewan
FundersSocial Sciences and Humanities Research Council of Canada
KeywordsNavyGeneral partnershipState (computer science)Work (physics)Private practicePublic administrationManagementPolitical sciencePublic relationsEconomic historyLawMedicineEngineeringHistoryFamily medicineEconomics

Abstract

fetched live from OpenAlex

Relations between the Royal Navy and London’s great hospitals during the late seventeenth and early eighteenth centuries concerning the care of sick and injured sailors were complicated, fraught and at times riven with conflict. This article surveys the relationship during the late Stuart era between the fiscal-naval state and the hospitals – semi-private institutions with a public mission – and attempts to re-evaluate the usefulness of the concept of partnership for understanding public and private interactions during wartime. The article contends that the state and the hospitals did not form a productive partnership because London’s hospitals were obliged to work for the navy in ways that compromised the hospitals’ ability to serve their core public.

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)0.004
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.163
Threshold uncertainty score0.325

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.004
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.002
Science and technology studies0.0040.008
Scholarly communication0.0070.004
Open science0.0010.004
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0060.001

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 head0.018
GPT teacher head0.213
Teacher spread0.195 · 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 designQualitative
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

Citations3
Published2016
Admission routes2
Has abstractyes

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