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Record W2409915878 · doi:10.32728/tab.13.2.2015.07

Pjesnikinje ili kurtizane? Predrasude o ženskom obrazovanju u renesansnim književnim djelima

2015· article· hr· W2409915878 on OpenAlexfundno aff
Martina Damiani

Bibliographic record

VenueTabula · 2015
Typearticle
Languagehr
FieldEconomics, Econometrics and Finance
TopicBalkan and Eastern European Studies
Canadian institutionsnot available
FundersUniversity of TorontoUniversity of Chicago
KeywordsHumanitiesArt

Abstract

fetched live from OpenAlex

I critici sono concordi nel riconoscere ai trattati pedagogici rinascimentali una propensione all’emancipazione culturale femminile. Il presente lavoro ha ricercato invece un altro aspetto, finora trascurato: i pregiudizi nei confronti dell’istruzione femminile presenti in alcune opere letterarie in concomitanza alla comparsa delle prime poetesse. Il preconcetto dello scrittore sembra nascere quando la donna diventa sua rivale in un campo di monopolio maschile: la letteratura. Avvalendosi dei gender studies si è dimostrato che diverse opere cinquecentesche identificano il silenzio femminile con la castità e l’abuso della parola con il malcostume. In particolare, la funzione della cortigiana, la prostituta colta e raffinata, è stata studiata mediante un approccio neostoricistico, che ha consentito di riconoscere in essa la rappresentante del pessimo influsso della cultura sulle donne. In quest’ottica, le competenze delle cortigiane come scrittrici appaiono messe in dubbio e ridotte a stratagemmi per ingannare gli uomini, mentre, in generale, nella denigrazione delle poetesse si cela la pretesa di ristabilire l’ordine sociale, compromesso dalla parte attiva che iniziavano a ricoprire le donne nel primo Cinquecento.

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)0.002
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 score0.062

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0060.011
Scholarly communication0.0080.004
Open science0.0010.006
Research integrity0.0010.003
Insufficient payload (model declined to judge)0.0190.002

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 head0.106
GPT teacher head0.233
Teacher spread0.127 · 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

Citations1
Published2015
Admission routes1
Has abstractyes

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