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Record W4389040173 · doi:10.9771/rf.v11i1.52593

A VIOLÊNCIA CONTRA AS MULHERES DURANTE A PANDEMIA DE COVID 19 EM PORTUGAL

2023· article· pt· W4389040173 on OpenAlexfundno aff
Dalila Cerejo, Maria Rosário Tomás Rosa, Ana Lúcia Teixeira, Wânia Pasinato, Manuel Lisboa

Bibliographic record

VenueRevista Feminismos · 2023
Typearticle
Languagept
FieldSocial Sciences
TopicIntimate Partner and Family Violence
Canadian institutionsnot available
FundersFederation for the Humanities and Social Sciences
KeywordsHumanitiesCoronavirus disease 2019 (COVID-19)Context (archaeology)Political scienceArtHistoryMedicine

Abstract

fetched live from OpenAlex

O contexto pandémico, particularmente o primeiro confinamento de 18 de março de 2020, trouxe uma enorme incerteza, tanto em Portugal como em todo o mundo, sobre o que estaria a acontecer às mulheres relativamente à sua exposição a situações de violência. Este artigo apresenta os principais dados obtidos no projeto O impacto da COVID-19 na violência contra as mulheres, desenvolvido em 2020. Através de uma amostra estatisticamente representativa de 1500 mulheres portuguesas (Continente e Regiões Autónomas) foi possível concluir que, durante o período do primeiro confinamento, as mulheres ficaram ainda mais expostas à violência do que anteriormente.

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)0.003
metaresearch head score (Gemma)0.002
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Science and technology studies, Insufficient payload (model declined to judge)
Consensus categoriesInsufficient payload (model declined to judge)
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.349
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.002
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.001
Science and technology studies0.0010.001
Scholarly communication0.0010.000
Open science0.0010.000
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0020.005

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 head0.060
GPT teacher head0.386
Teacher spread0.326 · 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
Published2023
Admission routes1
Has abstractyes

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