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Record W4408151945 · doi:10.22487/jpwkt.v1i1.4

Strategi Manajemen Pengembangan Taman Hutan Raya Kapopo Sebagai Ruang Terbuka Hijau Publik Kabupaten Sigi

2022· article· id· W4408151945 on OpenAlexaff
Noviana Talantan Noviana Talantan, Iwan Setiawan Basri, Ardiansyah Winarta Ardiansyah Winarta, Rosmiaty Arifin

Bibliographic record

VenueJurnal PeWeKa Tadulako · 2022
Typearticle
Languageid
FieldSocial Sciences
TopicCommunity-based Tourism Development and Sustainability
Canadian institutionsEncana (Canada)
Fundersnot available
KeywordsBusiness

Abstract

fetched live from OpenAlex

Taman Hutan Raya (Tahura) Kapopo Kabupaten Sigi dengan beraneka ragam potensi yang dapat dikembangkan. Tahura Kapopo selain sebagai kawasan konservasi, sebagian kawasannya dimanfaatkan sebagai wisata alam berupa ruang terbuka hijau publik yang dapat diakses masyarakat umum. Keberadaan Tahura Kapopo belum berkembang dengan baik serta banyak diketahui oleh masyarakat. Oleh karena itu, penelitian ini dilakukan untuk mengetahui bagaimana strategi pengelolaan atau manajemen pengembangan Tahura Kapopo sebagai tempat wisata alam ruang terbuka hijau publik. Penelitian ini menggunakan metode kualitatif deskriptif yang selanjutnya dianalisis melalui teknik SWOT. Hasil penelitian menghasilkan strategi manajemen; 1) meningkatkan dukungan dan peran pemerintah, 2) pelestarian sumber daya alam dan ekosistem, 3) memanfaatkan kerjasama guna meningkatkan kembali citra Tahura Kapopo, 4) meningkatkan kualitas dan wahana wisata, 5) alokasi dana anggaran yang cukup, 6) meningkatkan aksesibilitas serta ketersediaan angkutan umum, 7) meningkatkan kualitas SDM pengelola dan masyarakat sekitar, 8) menegakkan aturan dan pemberian sanksi bagi yang melanggar, 9) pembagian zonasi fungsi lahan yakni untuk kawasan konservasi hutan flora dan fauna dan areal ruang terbuka hijau publik (area wisata), 10) menyiapkan regulasi sektoral serta dokumen teknis lainnya terkait pengembangan Tahura Kapopo, 11) diperlukan pengawasan baik serta evaluasi secara berkala yang hasil menjadi bahan pertimbangan dalam rangka pengembangan, serta 12) meningkatkan koordinasi dengan aparat keamanan menunjang keamanan kawasan dan sekitarnya

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)0.006
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Science and technology studies, Scholarly communication, Research integrity, Insufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.470
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0060.000
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.002
Science and technology studies0.0080.001
Scholarly communication0.0020.001
Open science0.0030.002
Research integrity0.0000.003
Insufficient payload (model declined to judge)0.0110.000

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 head0.023
GPT teacher head0.275
Teacher spread0.253 · 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 teacher head, not a consensus.

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

Citations1
Published2022
Admission routes1
Has abstractyes

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