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Record W4390476203 · doi:10.32502/arsir.v7i2.6177

Perceptions of Subsidized Housing Based on Occupants in Makassar City

2023· article· id· W4390476203 on OpenAlexaff
Nonny Rifka Rizky Amelia, Arina Hayati, Muhammad Faqih

Bibliographic record

VenueArsir · 2023
Typearticle
Languageid
FieldSocial Sciences
TopicUrban and Rural Development Challenges
Canadian institutionsEncana (Canada)
Fundersnot available
KeywordsHumanitiesPhysicsArt

Abstract

fetched live from OpenAlex

Terbatasnya luas lahan dan meningkatnya jumlah penduduk membuat harga rumah di kota besar menjadi mahal sehingga penyediaan rumah subsidi ikut berkembang. Persoalan yang sering terjadi dalam rumah subsidi yaitu perancangan rumah yang tidak memandang keperluan dari penghuni tersebut. Rumah subsidi yang tidak sesuai dengan keinginan dapat mempengaruhi persepsi kenyamanan penghuni terhadap rumah subsidi yang tersedia. Tujuan penelitian ini untuk mengetahui persepsi penghuni di Kota Makassar pada hunian subsidi yang tersedia tergantung keinginan yang di butuhkan. Menggunakan Metode penelitian yaitu kuantitatif dengan kuesioner untuk mengetahui persepsi kenyamanan terhadap elemen arsitektur rumah subsidi dengan mempertanyakan beberapa aspek yang dapat dilihat dari karakteristik penghuni kemudian dianalisis menggunakan skala likert dengan cross tabulasi silang (SPSS) yang dihubungkan dengan karakteristik penghuni. Hasil penelitian diperoleh persepsi penghuni dicrosstabulasi dan dihubungkan dengan karakteristik penghuni menunjukkan bahwa persepsi dengan profil penghuni di Kota Makassar rata-rata merasa nyaman dengan ruang yang tersedia untuk aktivitasnya. Namun ada pula yang tidak nyaman sehingga mungkin membutuhkan penambahan atau perluasan ruang dan perubahan fungsi ruang dalam menunjang kebutuhannya sehari-hari

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 categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.163
Threshold uncertainty score0.788

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.001

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 head0.077
GPT teacher head0.327
Teacher spread0.250 · 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.

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".

Quick stats

Citations0
Published2023
Admission routes1
Has abstractyes

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