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Record W2110745408 · doi:10.1093/shm/hkj019

Krankenhaus und lokale Politik 1770–1850: Das Beispiel Düsseldorf

2006· article· de· W2110745408 on OpenAlexaff
Stephan Curtis

Bibliographic record

VenueSocial History of Medicine · 2006
Typearticle
Languagede
FieldSocial Sciences
TopicEuropean history and politics
Canadian institutionsMemorial University of Newfoundland
Fundersnot available
KeywordsInstitutionPower (physics)Political scienceMedical careHealth carePovertyEconomic historyHumanitiesNursingMedicineHistoryArtLaw

Abstract

fetched live from OpenAlex

Fritz Dross, Krankenhaus und lokale Politik 1770–1850: Das Beispiel Düsseldorf , Essen: Klartext, 2004. Pp. 400. €24.90. ISBN 3–89861–257–0. ‘Only the sick are poor’. This quotation from Dr Christoph Wilhelm Hufeland in 1809 sets the stage for this innovative examination of the provision of health care in Düsseldorf from 1780 to 1850. Fritz Dross argues that historians have often failed to examine the relationship between sickness and poverty, despite the fact that in the minds of nineteenth-century observers there was an obvious link between the two. By separating these themes, we inhibit our understanding of how small hospitals were transformed into substantially larger institutions by the mid-1800s. Dross is not interested in viewing the creation of the institution simply as a medical phenomenon consisting of doctors, patients, new methods of treatment and power relations. Instead, he is much more eager to reveal the functions the hospital and Krankenhaus was intended to serve while illuminating the rationale underlying changes made to the provision of medical care during the 80 years he examines. He contends that at each stage of its growth the primary institution responsible for providing medical care represented much more to the general public than a place where the sick could go to receive treatment.

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)0.002
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Science and technology studies, Insufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Other · Consensus signal: none
Teacher disagreement score0.568
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
Meta-epidemiology (narrow)0.0000.001
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0010.009
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0010.001
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 head0.033
GPT teacher head0.314
Teacher spread0.280 · 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 designNot applicable
Domainnot available
GenreOther

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
Published2006
Admission routes1
Has abstractyes

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