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Record W4402216664 · doi:10.36985/fex5g223

Pengaruh Objek Wisata Taman Hewan Terhadap Pengembangan Wilayah Kota Pematangsiantar

2020· article· id· W4402216664 on OpenAlexaff
Ita Yanthi Silalahi, Marihot Manullang, Robert Tua Siregar, Sarintan E Damanik

Bibliographic record

VenueJurnal Regional Planning · 2020
Typearticle
Languageid
FieldSocial Sciences
TopicCommunity-based Tourism Development and Sustainability
Canadian institutionsEncana (Canada)
Fundersnot available
KeywordsGeography

Abstract

fetched live from OpenAlex

Tujuan penelitian ini adalah menganalisis Pengaruh Objek Wisata Taman Hewan Terhadap Pengembangan Wilayah Kota Pematangsiantar. Keberhasilan pengembangan wilayah membutuhkan dukungan semua masyarakat. Populasi penelitian ini adalah penduduk masyarakat kecamatan Siantar Utara berjumlah 46.613 jiwa. Dengan mengggunakan rumus penarikan sampel, maka sampel penelitian sebesar 96 orang. Penelitian ini menggunakan regresi sederhana, metode analisis dan pengujian hipotesis. Penelitian ini memberikan informasi bahwa obyek wisata memiliki pengaruh terhadap pengembangan wilayah. Pengolahan data dilakukan dengan menggumpulkan data hasil kuesioner dan pengolahannya menggunakan SPSS. Dari hasil penelitian diketahui bahwa pengaruh obyek wisata terhadap pengembangan wilayah sebesar 0,325 atau 32,5 %. Jadi dapat disimpulkan bahwa obyek wisata mempunyai pengaruh yang signifikan terhadap pengembangan wilaya di Kecamatan Siantar Utara. Penelitian ini memberikan sumbangan pemikiran dan saran kepada Pemerintah Kecamatan Siantar Utara bahwa pengembangan wilayah membutuhkan dukungan penuh dari Pemerintah Kota Pematangsiantar

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)0.003
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: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.041
Threshold uncertainty score0.137

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.005
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0020.002
Science and technology studies0.0020.001
Scholarly communication0.0040.002
Open science0.0010.002
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0410.007

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 head0.064
GPT teacher head0.308
Teacher spread0.244 · 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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Citations0
Published2020
Admission routes1
Has abstractyes

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