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Record W7051651155

PENGELOLAAN INDUSTRI TENUN IKAT DI DESA PARENGAN KECAMATAN MADURAN KABUPATEN LAMONGAN (1973-1998)

2019· dissertation· id· W7051651155 on OpenAlexaff

Bibliographic record

VenueUniversitas Airlangga Repository (Universitas Airlangga) · 2019
Typedissertation
Languageid
FieldPhysics and Astronomy
TopicMagnetic confinement fusion research
Canadian institutionsWiLAN (Canada)
Fundersnot available
KeywordsProduction (economics)Work (physics)
DOInot available

Abstract

fetched live from OpenAlex

Industri tenun ikat di Desa Parengan Kecamatan Maduran Kabupaten
\nLamongan mengalami perkembangan seiring dengan penerapan sistem
\npengelolaan yang disesuaikan dengan kultur dan budaya masyarakat setempat.
\nPermasalahan yang dibahas dalam penelitian ini adalah bagaimana pengelolaan
\ndan faktor yang mengakibatkan pasang surut industri tenun ikat di Desa Parengan
\ntahun 1973-1998. Metode yang digunakan dalam penelitian ini adalah metode
\nsejarah, meliputi heuristik, kritik, interpretasi dan historiografi. Hasil penelitian
\nyang diperoleh menunjukkan bahwa dalam pengelolaannya industri tenun ikat di
\nDesa Parengan tahun 1973-1998 terdiri atas ketenagakerjaan, jaringan ketersedian
\nbahan baku, produksi, serta distribusi dan pemasaran. Pasang surut industri tenun
\nikat di Desa Parengan diukur dengan beberapa indikator, yaitu perkembangan
\ntenaga kerja, proses produksi dan pemasaran. Meningkatnya keempat elemen
\ntersebut industri berada pada masa kejayaan. Sebaliknya, keempat elemen tersebut
\nmenurun industri berada pada masa kelesuan. Kelesuan pada indutri tenun ikat di
\nDesa Parengan diakibatkan karena beberapa faktor yaitu bencana banjir,
\npersaingan usaha dengan sarung printing, cap dan sablon, adanya konflik di
\nnegara Pengimpor, serta diperparah dengan krisis moneter 1998. Industri tenun
\nikat Parengan untuk mempertahankan eksistensinya dalam dunia pertekstilan
\nharus melakukan inovasi. Inovasi dilakukan dengan melakukan penggandaan
\nfungsi produk, menyesuaikan dengan tren di pasaran, serta melakukan
\npembaharuan pada desain dan mode.

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), Science and technology studies, Research integrity, Insufficient payload (model declined to judge)
Consensus categoriesMeta-epidemiology (narrow), Research integrity, Insufficient payload (model declined to judge)
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.605
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0020.003
Meta-epidemiology (broad)0.0020.001
Bibliometrics0.0020.002
Science and technology studies0.0030.001
Scholarly communication0.0010.001
Open science0.0030.001
Research integrity0.0020.003
Insufficient payload (model declined to judge)0.0490.003

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 head0.010
GPT teacher head0.211
Teacher spread0.202 · 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 designNot applicable
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
Published2019
Admission routes1
Has abstractyes

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