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FAKTOR-FAKTOR YANG MEMPENGARUHI LEMBAGA PEMBERDAYAAN MASYARAKAT KECAMATAN TAMALANREA DALAM PERENCANAAN PEMBANGUNAN DAERAH DI KOTA MAKASSAR

2022· article· id· W4317366230 on OpenAlexaff
Hasran Hasran, Zaenal Akhmad

Bibliographic record

VenueJurnal Ilmiah Administrasita · 2022
Typearticle
Languageid
FieldSocial Sciences
TopicSMEs Development and Digital Marketing
Canadian institutionsEncana (Canada)
Fundersnot available
KeywordsHumanitiesPhysicsArt

Abstract

fetched live from OpenAlex

Penelitian ini bertujuan untuk mengetahui faktor-faktor yang berpengaruh dalam optimalisasi kinerja Lembaga Pemberdayaan Masyarakat di Kecamatan Tamalanrea Kota Makassar. Tipe penelitian yang digunakan adalah tipe penelitian kuantitatif deskriptif, Dasar penelitian ini adalah metode survey. Populasi adalah keseluruhan komponen yang menjadi objek penelitian, oleh karena itu yang menjadi objek penelitian adalah Pengurus LPM di Kecamatan Tamalanrea, pemerintahan kelurahan dan masyarakat di Kecamatan Tamalanrea. Penulis menggunakan teknik penarikan sampel secara purposive. Untuk memperoleh informasi yang sesuai dengan permasalahan yang diteliti akan digunakan : Data Primer dan Data sekunder. Adapun teknik pengumpulan data yang akan digunakan dalam penelitian ini adalah Kuisioner, Observasi, Study kepustakaan dan Dokumentasi.
 Data yang telah dikumpulkan melalui observasi, dokumentasi dan studi pustaka dalam penelitian ini dijadikan sebagai data sekunder, sedangkan data yang diperoleh dari kuisioner dianalisis secara kuantitatif dengan menggunakan program SPSS. Berdasarkan hasil Penelitian, didapatkan Faktor–faktor yang memengaruhi kinerja pengurus Lembaga Pemberdayaan Masyarakat adalah profesionalisme pengurus Lembaga Pemberdayaan Masyarakat, akurasi data dan informasi, sarana dan prasarana, koordinasi dan motivasi

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)0.004
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Science and technology studies, Scholarly communication, Research integrity, Insufficient payload (model declined to judge)
Consensus categoriesMeta-epidemiology (narrow)
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.432
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0040.001
Meta-epidemiology (narrow)0.0010.002
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.002
Science and technology studies0.0050.001
Scholarly communication0.0020.001
Open science0.0030.002
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0110.001

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 head0.026
GPT teacher head0.285
Teacher spread0.258 · 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; both teacher heads agree on what is shown here.

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".

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Citations0
Published2022
Admission routes1
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