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Keberlanjutan Penerapan Teknologi Pengelolaan Pekarangan oleh Wanita Tani di Kabupaten Kuningan

2017· article· id· W2610096541 on OpenAlexaff
Ani Suryani, Anna Fatchiya, Djoko Susanto

Bibliographic record

VenueJurnal Penyuluhan · 2017
Typearticle
Languageid
FieldAgricultural and Biological Sciences
TopicAgricultural Development and Management
Canadian institutionsEncana (Canada)
FundersInstitut Pertanian Bogor
KeywordsBusinessAgricultural scienceBusiness administrationMathematicsHumanitiesBiologyArt

Abstract

fetched live from OpenAlex

Keberlanjutan penerapan teknologi pengelolaan pekarangan oleh wanita tani pada dasarnya ditujukan guna menghadapi isu ketahanan pangan nasional, perbandingan ketersediaan pangan vs pertambahan jumlah penduduk, isu alih fungsi lahan dan kesadaran tentang pentingnya upaya diversifikasi pangan. Penelitian ini bertujuan: (1) menganalisis karakteristik individu, karakteristik inovasi, kinerja fasilitator, dukungan eksternal dan keberlanjutan penerapan teknologi pengelolaan pekarangan; (2) mengkaji pengaruh karakteristik individu, karakteristik inovasi, kinerja fasilitator, dan dukungan eksternal terhadap keberlanjutan penerapan teknologi pengelolaan pekarangan. Penelitian dilakukan di Kecamatan Sindangagung dan Kecamatan Jalaksana, Kabupaten Kuningan Provinsi Jawa Barat. Sampel penelitian berjumlah 76 orang wanita tani. Hasil analisis regresi linear berganda Uji F (simultan), semua peubah bebas karakteristik individu, karakteristik inovasi, kinerja penyuluh/fasilitator dan dukungan lingkungan eksternal memiliki pengaruh nyata terhadap keberlanjutan adopsi. Nilai pengaruh sebesar 72,4% sedangkan sisanya 27,6% dipengaruhi oleh peubah lain yang tidak ada di dalam model regresi. Secara berurutan indikator peubah karakteristik individu yang berpengaruh nyata adalah umur, motivasi, jumlah anggota keluarga, tingkat pendidikan, curahan waktu wanita tani dan pendapatan keluarga. Indikator karakteristik inovasi adalah keuntungan relatif dan tingkat kesesuaian inovasi. Indikator kinerja fasilitator adalah tingkat kunjungan dan tingkat pengetahuan. Semua indikator dukungan eksternal pemasaran, dukungan keluarga, dukungan kelompok dan sarana prasarana berpengaruh nyata terhadap keberlanjutan pengelolaan lahan pekarangan.

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)0.001
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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.031
Threshold uncertainty score0.104

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0020.001
Scholarly communication0.0040.002
Open science0.0010.002
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0310.004

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 head0.030
GPT teacher head0.238
Teacher spread0.208 · 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".

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Citations19
Published2017
Admission routes1
Has abstractyes

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