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Record W4397027491 · doi:10.61731/dpmr.v3i1.31517

Menavigasi Kemiskinan Di Kepulauan: Studi Kasus Di Kepulauan Romang, Maluku Barat Daya

2024· article· id· W4397027491 on OpenAlexaff
Nur Ovaliani, Gusti Ayu Putu Candra Dewi, Riki Irmawan, Salma Nisa Adiyani, Fera Perdina Waani

Bibliographic record

VenueDevelopment Policy and Management Review (DPMR) · 2024
Typearticle
Languageid
FieldEconomics, Econometrics and Finance
TopicEconomic Growth and Fiscal Policies
Canadian institutionsEncana (Canada)
Fundersnot available
KeywordsPolitical scienceArt

Abstract

fetched live from OpenAlex

Kemiskinan masih menjadi salah satu masalah sosial dan perhatian utama Pemerintah Indonesia untuk menanggulanginya. Isu kemiskinan menjadi sangat kompleks di wilayah kepulauan. Artikel ini mengambil kasus di salah satu pulau di Kabupaten Maluku Barat Daya, Provinsi Maluku. Kemiskinan di Kepulauan Romang disebabkan oleh kondisi alamiah dan ekonomi, kondisi struktural dan sosial, serta kondisi kultural (budaya). Kemiskinan alamiah dan ekonomi timbul akibat keterbatasan sumber daya alam, kualitas sumber daya manusia, dan sumberdaya lain sehingga peluang produksi relatif kecil dan tidak dapat berperan dalam pembangunan. Kemiskinan struktural dan sosial disebabkan hasil pembangunan yang belum merata, sarana dan prasarana kurang memadai, tatanan kelembagaan dan kebijakan dalam pembangunan yang belum optimal. Sedangkan kemiskinan kultural (budaya) disebabkan sikap atau kebiasaan hidup yang merasa kecukupan sehingga menjebak seseorang dalam kemiskinan dan budaya lokal yang kadangkala bertentangan dengan kegiatan pembangunan di kepulauan Romang. Aksesibilitas pada wilayah kepulauan Romang yang sulit akibat topografi wilayah yang terpisah oleh lautan dan merupakan pulau kecil. Selain itu, kondisi kemiskinan di Pulau Romang menjadi lebih kompleks dengan adanya aktivitas pertambangan emas di pulau yang berdampak pada lingkungan dan dinamika ekonomi masyarakat pulau. Artikel in menganalisis masalah sosial kemiskinan di Pulau Romang dengan memetakan akar masalah untuk mengindentifikasi solusi sebagai landasan dalam mendesain perencanaan sosial dan desain keterlibatan sosial untuk menanggulangi masalah kemiskinan yang kompleks.

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)0.002
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.028
Threshold uncertainty score0.057

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0030.001
Scholarly communication0.0030.002
Open science0.0010.002
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0100.002

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 head0.028
GPT teacher head0.262
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 source (direct Gemma or distilled Codex), 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
Published2024
Admission routes1
Has abstractyes

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