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Perencanaan Pembangunan Fasilitas Publik Berbasis Masyarakat: Studi Kasus Pembangunan Bendungan Seratak di Kabupaten Kotabaru

2025· article· W7124840537 on OpenAlexaff
Rahmat Nur, Erlina Erlina, Rachmat Hidayat, Ismar Hamid, Yusril Yusril, Muhammad Rifani

Bibliographic record

VenueJurnal Ilmiah Muqoddimah Jurnal Ilmu Sosial Politik dan Hummaniora · 2025
Typearticle
Language
FieldEnvironmental Science
TopicCoastal Management and Development
Canadian institutionsEncana (Canada)
Fundersnot available
KeywordsResearch method

Abstract

fetched live from OpenAlex

Penelitian ini bertujuan untuk menganalisis proses perencanaan pembangunan Bendungan Seratak dengan menekankan peran masyarakat dalam setiap tahapannya. Metode penelitian menggunakan pendekatan studi kasus dengan pengumpulan data melalui wawancara mendalam, diskusi kelompok terfokus (FGD), serta analisis dokumen, termasuk Detail Engineering Design (DED) dan studi kelayakan yang disusun oleh Dinas PUPR dan Bappeda. Hasil penelitian menunjukkan bahwa Sungai Seratak memiliki debit air yang cukup besar (600–700 l/detik) dengan kualitas air yang sebagian besar memenuhi standar baku, sehingga layak dijadikan sumber utama pasokan air. Selain itu, keterlibatan masyarakat dalam identifikasi kebutuhan air bersih dan irigasi meningkatkan efektivitas desain proyek dan menumbuhkan rasa memiliki terhadap fasilitas publik. Dukungan politik dari DPRD dan pemerintah pusat turut memperkuat potensi keberhasilan pembangunan ini. Kesimpulan penelitian ini menegaskan bahwa perencanaan berbasis masyarakat memberikan kontribusi signifikan terhadap keberlanjutan dan penerimaan sosial proyek. Rekomendasi utama

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)0.002
metaresearch head score (Gemma)0.005
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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.014
Threshold uncertainty score0.048

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.005
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0020.001
Scholarly communication0.0030.003
Open science0.0010.002
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0140.002

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 head0.015
GPT teacher head0.257
Teacher spread0.242 · 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".

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Citations0
Published2025
Admission routes1
Has abstractyes

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