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Record W4401403674 · doi:10.33137/ijidi.v8i2.43500

Quilombola women confronting digital discrimination in Brazil

2024· article· en· W4401403674 on OpenAlexfundno aff
Ivonete da Silva Lopes, Daniela de Ulysséa Leal

Bibliographic record

VenueThe International Journal of Information Diversity & Inclusion (IJIDI) · 2024
Typearticle
Languageen
FieldSocial Sciences
TopicDigital Economy and Work Transformation
Canadian institutionsnot available
FundersConselho Nacional de Desenvolvimento Científico e TecnológicoUniversity of TorontoFundação de Amparo à Pesquisa do Estado de Minas GeraisMinistério da Ciência, Tecnologia e Inovação
KeywordsGender studiesSociology

Abstract

fetched live from OpenAlex

This work discusses the relationship between digital disconnection and the violation of rights in Quilombola territories. Quilombolas are an ethnic-racial group with Black origins associated with oppression and resistance over the centuries in Brazil. The research was conducted with 41 women aged between 18 and 73 who are leaders in 35 remaining Quilombola communities in Minas Gerais, Brazil. The impacts of limited access to information and communications technology (ICT) were assessed using an intersectional perspective, considering social markers of difference such as gender, social class, ethnic-racial belonging, and territory. The results show that 11 territories lack internet service, significantly restricting access to information, health, education, and social participation. The research points to the need for considering digital inequity as another matrix of intersectional oppression, as being disconnected or having restrictions on access and use of ICT increases the vulnerability of these territories, especially for the Quilombola women.

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)0.003
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.045
Threshold uncertainty score0.089

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0050.003
Scholarly communication0.0020.001
Open science0.0000.003
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0040.000

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 head0.010
GPT teacher head0.263
Teacher spread0.253 · 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 designQualitative
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

Citations3
Published2024
Admission routes1
Has abstractyes

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