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Record W4408174854 · doi:10.5430/ijba.v16n1p83

Women Who Transform: A Study on Female Leadership in Volunteer Associations on the Coast of Paraná – Brazil

2025· article· en· W4408174854 on OpenAlexvenueno aff
Adilson Anacleto, Aline Pereira de Souza, Ana Carolina da Silva Soares, Maria Gabriele Araújo Galdino, Luciane Scheuer, Luís Fernando Roveda, Larissa do Rosário Lopes Marques

Bibliographic record

VenueInternational Journal of Business Administration · 2025
Typearticle
Languageen
FieldSocial Sciences
TopicSocial and Economic Solidarity
Canadian institutionsnot available
FundersSecretário de Ciência, Tecnologia e Ensino Superior, Governo do Estado de Parana
KeywordsVolunteerPsychologyDemographic economicsDemographyBusinessSociologyEconomicsBiologyEcology

Abstract

fetched live from OpenAlex

Female leadership in Brazil has been gaining ground in several areas of society, especially when related to social issues. Recent data show that almost half of the leadership positions in this type of organization are held by women. Thus, this study aimed to investigate the occurrence of female leadership in social associations for women on the Coast of Paraná, Brazil, in communities in situations of socioeconomic vulnerability. A descriptive, exploratory study with qualitative nature was conducted with 10 female leaders on the Coast of Paraná. The results of the research revealed that most female leaders do not have formal support from government institutions, and that partnerships with institutions and other leaders are the main resources and agents of transformation. The main factors attributed to this resilience are the love of volunteering, persistence, and the dissemination of activities that generates visibility in the media and helps new volunteers to come to the associations. The services offered by these organizations are entirely free, provided close to the houses of the women benefiting from them and have a positive impact on the access to education, employment opportunities, medical care and increased income for these women, promoting a more promising future outlook.

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)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.239
Threshold uncertainty score0.275

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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 head0.130
GPT teacher head0.364
Teacher spread0.234 · 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 teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
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
Published2025
Admission routes1
Has abstractyes

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