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Record W4390471515 · doi:10.47200/aoej.v15i1.2011

Strategi penanganan korban kekerasan seksual pada perempuan oleh Dinas Pemberdayaan Perempuan, Perlindungan Anak, Pengendalian Penduduk dan Keluarga Berencana Kabupaten Karanganyar

2023· article· en· W4390471515 on OpenAlexaff
Yesita Amanda, Triana Rekejiningsih, Erna Yuliandari

Bibliographic record

VenueAcademy of Education Journal · 2023
Typearticle
Languageen
FieldSocial Sciences
TopicLegal and Social Justice Studies
Canadian institutionsEncana (Canada)
Fundersnot available
KeywordsEmpowermentPopulationDomestic violenceSexual violenceOutreachPsychologyMedicineNursingPoison controlSuicide preventionPolitical scienceMedical emergencyEnvironmental healthLaw

Abstract

fetched live from OpenAlex

This study aims to analyze: 1) What is the strategy for handling victims of sexual violence against women by the Office of Women's Empowerment, Child Protection, Population Control and Family Planning, Karanganyar Regency. 2) What are the problems encountered in handling victims of sexual violence against women and the solutions. In this study using qualitative research methods. In this study the results were found: 1) strategies for handling victims of sexual violence against women by the Office of Women's Empowerment, Child Protection, Population Control and Family Planning in Karanganyar Regency, namely (1) receiving reports, (2) outreach to victims, (3) case processing, ( 4) mediation, (5) medical, psychological, legal assistance, (6) psychological and trauma recovery. 2) The problems encountered in handling victims of sexual violence against women and their solutions are (1) lack of companion staff, namely by engaging other institutions that have the same goals, (2) victims who are not open, overcome with refreshing minds, (3) families who lack cooperative given an understanding, (4) the lack of understanding of the community about the agency that handles cases of sexual violence, overcome by conducting socialization.

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)0.002
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Science and technology studies
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.220
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.001
Science and technology studies0.0030.001
Scholarly communication0.0000.001
Open science0.0010.000
Research integrity0.0000.002
Insufficient payload (model declined to judge)0.0010.000

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 head0.068
GPT teacher head0.384
Teacher spread0.316 · 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 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

Citations0
Published2023
Admission routes1
Has abstractyes

Explore more

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