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Record W2783087113

Imagining Girlhood in Seventeenth-Century Female-Authored Fairytales

2017· article· en· W2783087113 on OpenAlexaffvenue
Meghan Kort

Bibliographic record

VenueThe Graduate History Review · 2017
Typearticle
Languageen
FieldArts and Humanities
TopicFolklore, Mythology, and Literature Studies
Canadian institutionsUniversity of Victoria
Fundersnot available
KeywordsVirtueBeautyGirlContext (archaeology)SociologyDIDOSalonGender studiesAestheticsLiteratureHistoryArtPsychologyPhilosophyAnthropologyDevelopmental psychologyTheologyArchaeology
DOInot available

Abstract

fetched live from OpenAlex

Over two-thirds of the fairytales published in late seventeenth-century France were authored by female conteuses  who recount the births of beautiful and virtuous princesses. Although little is known about these authors' personal lives, their portrayals of girlhood reveal glimpses of their individual lives and experiences. Applying Robert Darnton's cultural approach to fairytales, I situate these tales and their tellers within their historical context. In each story, the girls' virtue or vice are not developed over time, but are embedded at birth, demarcated by beauty or ugliness. This entwining of beauty and virtue is typical of late seventeenth-century salon and educational writings. However, the conteuses'  girl characters also challenge gendered stereotypes, playing assertive roles and holding authority over older male characters. The conteuses  crafted their conceptions of girlhood in dialogue with individual and cultural influences, culminating in a shared conception of noble girls as virtuous, beautiful, and capable individuals.

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)0.003
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 score0.019
Threshold uncertainty score0.038

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0030.002
Science and technology studies0.0060.011
Scholarly communication0.0060.004
Open science0.0010.003
Research integrity0.0020.002
Insufficient payload (model declined to judge)0.0030.000

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 head0.132
GPT teacher head0.295
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
Published2017
Admission routes2
Has abstractyes

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