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Record W3210319092 · doi:10.29040/budimas.v3i2.3102

PELATIHAN PERENCANAAN SUMBER DAYA LOKAL DALAM MENYONGSONG KAWASAN INDUSTRI TERPADU BATANG

2021· article· id· W3210319092 on OpenAlexaff
Putranto Hari Widodo, Suparno Suparno, Neli Hajar

Bibliographic record

VenueBUDIMAS JURNAL PENGABDIAN MASYARAKAT · 2021
Typearticle
Languageid
FieldSocial Sciences
TopicSMEs Development and Digital Marketing
Canadian institutionsEncana (Canada)
Fundersnot available
KeywordsPolitical scienceHumanitiesArt

Abstract

fetched live from OpenAlex

Tujuan kegiatan pengabdian kepada masyarakat ini adalah untuk mengedukasi masyarakat tentang pentingnya perencanaan sumberdaya lokal dalam menyongsong Kawasan Industri Terpadu Batang dengan cara perencanaan, analisis dan peluang kerja di Kabupaten Batang. Metode pelaksanaan pengabdian kepada masyarakat ini dilakukan melalui penyampaian materi yang berkaitan dengan Sumber daya lokal mulai dari perencanaan sumber daya manusia, metode, informasi, dan peramalan PSDM sampai dengan proses perencanaan sumber daya manusia. Waktu pelaksanaan pelaksanaan pengabdian kepada masyarakat ini pada tanggal 6 Juni 2021 bertempat di Kantor Forum Komunikasi Peduli Batang (FKPB) di Jl. Raya Banyuputih-Limpung KM.1 RT 08 RW. 02, Lokojoyo, Ds. Banyuputih, Kec. Banyuputih, Kab. Batang. Hasil pengabdian kepada masyarakat ini menunjukkan bahwa masyarakat teredukasi baik dengan adanya kegiatan ini. Dimana yang semula masyarakat dalam melaksanakan sesuatu dilakukan secara tidak terencana hanya sebatas melihat apa yang biasanya umum terjadi menjadi paham dan sadar akan pentingnya suatu perencanaan dan pemanfaatan sumberdaya lokal secara optimal dalam mendukung potensi ekonomi lokal.
 
 Kata Kunci: sumber daya lokal, potensi ekonomi, kawasan industri terpadu Batang

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)0.002
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Science and technology studies, Scholarly communication, Insufficient payload (model declined to judge)
Consensus categoriesInsufficient payload (model declined to judge)
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.431
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.002
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0000.002
Science and technology studies0.0020.001
Scholarly communication0.0020.002
Open science0.0010.001
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0060.001

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 head0.031
GPT teacher head0.274
Teacher spread0.243 · 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".

Quick stats

Citations0
Published2021
Admission routes1
Has abstractyes

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