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Record W7039534764

Medycyna i ułomności ciała w lirykach i epigramatach Alberta Inesa

2020· article· en· W7039534764 on OpenAlexaboutno aff

Bibliographic record

VenueJagiellonian University Repository (Jagiellonian University) · 2020
Typearticle
Languageen
FieldArts and Humanities
TopicHistorical and Cultural Studies of Poland
Canadian institutionsnot available
Fundersnot available
KeywordsSubject (documents)Data collectionPerspective (graphical)Object (grammar)
DOInot available

Abstract

fetched live from OpenAlex

Albert Ines (1619–1649) was one of the most important Polish-Latin poets of the 17th century. The subject of this text is the handling of the themes of medicine, health and disease in his work on the example of odes (“Lyricorum centuria”) and the collection of epigrams. The article analyses his disparaging jokes about physicians, popular in Roman and Greek tradition, and onomastic jokes about health and caricatural imagery of the body, i.e. obesity, lack of teeth, baldness or wigs. It also points at the figures of speech pertaining to the portrayal of the frailty of the human condition.

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: none
Teacher disagreement score0.014
Threshold uncertainty score0.034

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0020.002
Science and technology studies0.0030.003
Scholarly communication0.0040.002
Open science0.0000.002
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0100.002

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 head0.017
GPT teacher head0.139
Teacher spread0.122 · 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
Published2020
Admission routes1
Has abstractyes

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