MétaCan
Menu
Back to cohort
Record W4408586204 · doi:10.71128/e-gov.v2i3.126

EVALUASI PEMBANGUNAN BERKELANJUTAN MELALUI PEMBERDAYAAN EKONOMI BAGI MANTAN KOMBATAN DAN MASYARAKAT KORBAN KONFLIK DI PROVINSI ACEH

2024· article· id· W4408586204 on OpenAlexaff
Elva Rahmi

Bibliographic record

VenueJournal Education and Government Wiyata · 2024
Typearticle
Languageid
FieldEconomics, Econometrics and Finance
TopicEconomic Growth and Fiscal Policies
Canadian institutionsEncana (Canada)
Fundersnot available
KeywordsSociologyBusiness

Abstract

fetched live from OpenAlex

Reintegrasi bagi mantan kombatan dan masyarakat korban konflik merupakan konsekuensi dari penandatanganan nota kesepakatan antara pemerintah RI dengan GAM. Reintegrasi dilaksanakan dengan program pemberdayaan ekonomi, namun implementasinya belum menunjukkan hasil yang signifikan khususnya dalam hal peningkatan kesejahteraan. Tujuan dari kajian ini adalah untuk mendeskripsikan dan menganalisa evaluasi atas pembangunan berkelanjutan melalui pemberdayaan ekonomi mantan kombatan dan masyarakat korban konflik di Provinsi Aceh. Kajian ini dilakukan dengan menggunakan pendekatan kualitatif, dan data dievalausi dengan menggunakan model CIPP. Hasil kajian: hambatan dalam implementasi pemberdayaan ekonomi mantan kombatan dan masyarakat korban konflik terjadi dalam hal keterbatasan SDM pada Badan Reintergrasi Aceh, tidak adanya validitas data, indikasi potensi adanya penyelewengan dan penyalahgunaan anggaran, pemberian bantuan tidak tepat sasaran, perilaku curang dalam penggunaan dan pemanfaatan anggaran tidak sesuai peruntukan. Dengan demikian untuk melakukan pembangunan berkelanjutan dibutuhkan pemahaman makna reintegrasi, mantan kombatan dan masyarakat korban konflik untuk menghindari pembengkakan jumlah kelompok sasaran, pemberdayaan ekonomi harus saling bersinergi dengan pembangunan psikososial, dan diperlukannya adanya sinkronisasi dan sinergi antara pemerintah, swasta dan masyarakat.

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), Scholarly communication
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.546
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0010.000
Scholarly communication0.0020.002
Open science0.0010.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0000.001

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 head0.016
GPT teacher head0.243
Teacher spread0.227 · 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

Citations0
Published2024
Admission routes1
Has abstractyes

Explore more

Same venueJournal Education and Government WiyataSame topicEconomic Growth and Fiscal PoliciesFrench-language works237,207