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Record W4292209145 · doi:10.35138/gp.v3i2.353

PENGEMBANGAN EDUWISATA DI KAMPUS UNIVERSITAS WINAYA MUKTI, SUMEDANG

2021· article· id· W4292209145 on OpenAlexaff
Sigit Wisnuadji, Achmad Saeful Fasa

Bibliographic record

VenueGEOPLANART · 2021
Typearticle
Languageid
FieldComputer Science
TopicBlockchain Technology in Education and Learning
Canadian institutionsEncana (Canada)
Fundersnot available
KeywordsHumanitiesArt

Abstract

fetched live from OpenAlex

Kedekatan perguruan tinggi (kampus) dengan masyarakat adalah hal penting agar kampus dapat lebih dikenal. Dan dari kondisi seperti inilah diharapkan terjadi hubungan yang saling memberi manfaat, baik masyarakat maupun perguruan tinggi tersebut. Salah satu strategi yang dapat diterapkan adalah pengembangan kampus sebagai kawasan wisata edukasi atau eduwisata. Eduwisata yang dimaksud disini adalah pengembangan kegiatan yang berbasis pendidikan, pelatihan ataupun hasil penelitian yang dapat menjadi daya tarik untuk masyarakat untuk datang dan berkunjung, baik untuk mengikuti berbagai kegiatan akademik (pendidikan, pelatihan, seminar, penelitian) maupun menikmati hasilhasil karya civitas akademika. Metoda penelitian yang digunakan adalah metode penelitian deskriptif dan observasi langsung. Dengan dilakukan penataan program yang baik didukung oleh penataan fisik kampus yang baik, tentunya program ini diharapkan dapat mencapai tujuan dalam rangka untuk mencapai visi dan misi yang telah dicanangkan. Dengan didukung oleh kondisi lingkungan dan lokalitasnya, kampus Universitas Winaya Mukti yang merupakan salah satu universitas pertanian tertua di Indonesia ini berpotensi untuk dikembangkan sebagai kawasan eduwisata melalui penataan kawasan kampus yang dapat menggali segala potensi yang ada. Akar pertanian dan kehutanan yang kuat dapat menjadi salah satu karakter dari kampus ini. Gagasan fisik pengembangan yang ada harus didukung adanya sinergi antar civitas akademika kampus Universitas Winaya Mukti Sumedang

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: Not applicable · Consensus signal: none
GenreCandidate signal: Other · Consensus signal: Other
Teacher disagreement score0.050
Threshold uncertainty score0.167

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.001
Science and technology studies0.0030.001
Scholarly communication0.0030.001
Open science0.0010.002
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0500.014

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 head0.012
GPT teacher head0.232
Teacher spread0.220 · 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 designNot applicable
Domainnot available
GenreOther

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

Citations1
Published2021
Admission routes1
Has abstractyes

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