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Record W4312187208 · doi:10.31385/jl.v21i2.306.162-175

Peranan Gereja Katolik Dalam Penanganan Masalah Kekerasan Terhadap Perempuan Di Keuskupan Maumere Dan Larantuka, Flores, NTT

2022· article· en· W4312187208 on OpenAlexaff
Robert Mirsel, Yosef Keladu Koten, Ignasius Ledot

Bibliographic record

VenueJurnal Ledalero · 2022
Typearticle
Languageen
FieldHealth Professions
TopicAdolescent Health and Behaviors
Canadian institutionsWiLAN (Canada)
Fundersnot available
KeywordsEmpowermentTransformative learningCommissionDomestic violenceSociologyPolitical scienceGender studiesLawMedicinePoison controlSuicide preventionPedagogy

Abstract

fetched live from OpenAlex

<p><strong>ABSTRACT:</strong></p><p>This article talks about the problem of violence against women in the Maumere and Larantuka dioceses, Flores, NTT and how religion (Catholic Church) functions in dealing with it. The aim is to identify the forms of violence, their causes and effects in both dioceses and what the function of religion (Catholic Church) is to prevent and deal with these problems. Interview methods, focused group discussions (FGD) and document studies were used to collect data, while descriptive qualitative analysis was used for data analysis. The results show that violence against women is found in both areas of the diocese with physical, psychological, verbal, economic, cultural-based and online violence. The Catholic Church through the Diocesan Commission on Gender and Women's Empowerment carries out educational, control, fraternal, and transformative functions. This is done in direct collaboration and through networks with multi-stakeholders, although this is not sufficient. This study recommends the importance of strengthening institutional capacity and increasing network collaboration.</p><p><strong> </strong></p><p><strong>KEYWORDS:</strong> Commission on Gender and Women's Empowerment, institutional capacity, network collaboration, violence against women, women empowerment.</p>

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)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Science and technology studies, Research integrity, Insufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.342
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.001
Science and technology studies0.0060.000
Scholarly communication0.0000.000
Open science0.0010.001
Research integrity0.0000.004
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 head0.053
GPT teacher head0.379
Teacher spread0.325 · 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.

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

Citations1
Published2022
Admission routes1
Has abstractyes

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