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Holofotes sobre o sorrateiro gaslighting misógino

2025· article· pt· W4408616686 on OpenAlexaff
Antonio Camargo, Beatriz Maria Braga

Bibliographic record

VenueGV executivo \b (Impresso)/GV Executivo · 2025
Typearticle
Languagept
FieldAgricultural and Biological Sciences
TopicLight effects on plants
Canadian institutionsCanadian Association for the Study of Adult Education
Fundersnot available
KeywordsComputer science

Abstract

fetched live from OpenAlex

Objetivo: discutir a aplicação de conceito de gaslightingmisógino nas relações de trabalho, estabelecer seus elementos caracterizadores e o modo como ocorre no ambiente empresarial. Estado da arte: empresas investem somas consideráveis para encontrar e reter talentos. Contudo, parte desse investimento se perde com práticas de assédio moral, entre elas, o gaslighting misógino. Mulheres vítimas desse gaslighting – entendido como a manipulação de chefes, que gera desequilíbrio nas funcionárias – tendem a não se desenvolver na empresa ou a pedir demissão. Originalidade: a pesquisa mostra a importância de adotar um conceito de gaslighting adaptado ao ambiente empresarial, não restrito à prova de comprometimento da sanidade mental, como definido pelo Conselho Nacional de Justiça. O estudo também consegue caracterizar toda a dinâmica do gaslighting misógino, para facilitar a identificação de sua ocorrência. Impactos: a pesquisa ajuda funcionários, gestores e membros do Poder Judiciário a compreender o processo do gaslighting misógino nas relações de trabalho e apresenta recomendações para lidar com esse tipo de assédio.

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)0.005
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 score0.041
Threshold uncertainty score0.137

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.005
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0020.002
Science and technology studies0.0040.003
Scholarly communication0.0070.005
Open science0.0010.005
Research integrity0.0020.002
Insufficient payload (model declined to judge)0.0410.004

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 head0.013
GPT teacher head0.246
Teacher spread0.233 · 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
Published2025
Admission routes1
Has abstractyes

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