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Record W4412467587 · doi:10.22487/peweka.v2i1.13

Perubahan Harga Lahan di Kelurahan Baiya Pasca Penetapan Kawasan Ekonomi Khusus (KEK) Palu

2023· article· id· W4412467587 on OpenAlexaff
Chalifah Chalifah, Aziz Budianta, Khairin Rahmat

Bibliographic record

VenueJurnal PeWeKa Tadulako · 2023
Typearticle
Languageid
FieldEconomics, Econometrics and Finance
TopicEconomic Growth and Fiscal Policies
Canadian institutionsEncana (Canada)
Fundersnot available
KeywordsPhysics

Abstract

fetched live from OpenAlex

Pembangunan ataupun perkembangan suatu wilayah pasti akan memiliki pengaruh pada wilayah sekitarnya juga sama halnya dengan penetapan Kawasan Ekonomi Khusus Palu pada Kelurahan Baiya pasti berdamapak pada wilayah sekitar Kelurahan Baiya. Penelitian ini sendiri bertujuan untuk mencari tahu pengaruh dari Kawasan Ekonomi Khusus Palu terhadap perubahan harga lahan di Kelurahan Baiya yang dilihat dari penetapan kawasan ekonomi khusus dan juga perubahan penggunaan lahan dari tahun sebelum adanya penetapan Kawasan Ekonomi Khusus hingga tahun eksisting saat ini secara periodik. Penelitian ini menggunakan jenis penelitian kualitatif dengan teknik analisis data perubahan penggunaan lahan dan perubahan harga lahan. Berdasarkan hasil analisis yang dilakukan bahwa Kelurahan Baiya mengalami perubahan penggunaan lahan serta perubahan harga lahan dari sebelum adanya Kawasan Ekonomi Khusus hingga setelah adanya Kawasan Ekonomi Khusus. Perubahan yang terjadi tersebut dikarenakan Kawasan Ekonomi Khusus memiliki pengaruh terhadap harga lahan pada Kelurahan Baiya yang mana hal ini juga didukung oleh perubahan penggunaan lahan yang ada pada wilayah tersebut dan juga didukung oleh faktor-faktor perubahan harga lahan lainnya seperti faktor fisik wilayah, faktor sosial wilayah, faktor lokasi dan aksesibilitas wilayah, dan lain lain.

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), Scholarly communication, Insufficient payload (model declined to judge)
Consensus categoriesMeta-epidemiology (narrow), Insufficient payload (model declined to judge)
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.282
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0010.002
Meta-epidemiology (broad)0.0020.001
Bibliometrics0.0020.001
Science and technology studies0.0010.001
Scholarly communication0.0010.001
Open science0.0020.001
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0010.024

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 head0.033
GPT teacher head0.225
Teacher spread0.192 · 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 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".

Quick stats

Citations0
Published2023
Admission routes1
Has abstractyes

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