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Record W6990515774

Dynasty as a Patchwork House, or the (Evil) Stepmother: The Example of Zofia Jagiellonka

2019· article· en· W6990515774 on OpenAlexaboutno aff

Bibliographic record

VenueRepository of the Academy's Library (Library of the Hungarian Academy of Sciences) · 2019
Typearticle
Languageen
FieldArts and Humanities
TopicHistorical and Cultural Studies of Poland
Canadian institutionsnot available
Fundersnot available
KeywordsCompetitor analysisSocial relationWelfareStepfamilyGift givingSocial relationshipNegotiationConsciousnessSocial orderSocial group
DOInot available

Abstract

fetched live from OpenAlex

The significant age difference between Princess Zofia Jagiellonka and her husband had as one advantage for the princess that she had no competitors within her age group (e.g. a stepmother). Moreover, her stepdaughters were approximately the same age and, after her husband’s death, she found herself in similar circumstances to the as a widow. Zofia Jagiellonka eventually resolved the long-standing relationship between her husband and his mistress, knowing in this regard how to defend her social position. She consciously took up the role of mediator among the relatives, and she had a mitigating effect on the tensions between father and son. Her social consciousness included providing for the welfare of the new family by meeting the expectations placed on her with regards to her stepchildren. Her life was not that of the stereotypical “evil stepmother.” Rather, she was someone from whom her stepchildren and others repeatedly sought counsel. Through her royal birth, she was (with regard to her social status) superior to her Guelph relatives, and she had the king—her brother—as her protector. In terms of her relationship to her stepchildren, it was perhaps a great advantage that she herself bore no children, and thus there was no competitive milieu at the court in Wolfenbüttel.

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)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesScience and technology studies, Open science
Consensus categoriesScience and technology studies
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.652
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0000.001
Science and technology studies0.0020.005
Scholarly communication0.0000.002
Open science0.0060.002
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0010.000

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 head0.029
GPT teacher head0.216
Teacher spread0.187 · 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; both teacher heads agree on what is shown here.

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

Citations1
Published2019
Admission routes1
Has abstractyes

Explore more

Same venueRepository of the Academy's Library (Library of the Hungarian Academy of Sciences)Same topicHistorical and Cultural Studies of PolandFrench-language works237,207