DOMAIN TO DOMAIN TRANSFER SEBAGAI METODE PADA PERANCANGAN FASILITAS BAGI DISABILITAS NETRA
Bibliographic record
Abstract
Proses perancangan dalam arsitektur tidak luput dari pengguna yang terlibat pada bangunan atau lingkungan yang dirancang. Permasalahan arsitektur dan disabilitas tidak hanya sebatas penyelesaian teknis belaka. Keterlibatan pengguna disabilitas seharusnya bukan sekedar sebagai objek namun juga dapat membantu mengembangkan gagasan desain yang lebih kreatif. Pada penelitian ini mengembangkan persepsi disabilitas netra terhadap lingkungan alam hingga terbentuk kriteria arsitektural melalui metode domain-to domain transfer. Metode ini pada dasarnya mengacu pada prinsip dasar metode analogi pada arsitektur dimana terdiri dari sumber , transfer/proses reduksi dan target. Sumber diambil melalui kajian penelitian sebelumnya mengenai pengalaman sensori disabilitas netra terhadap ruang luar (outerspace) seperti pantai dan kebun raya. Melalui proses reduksi dihasilkan beberapa kriteria desain untuk rancangan fasilitas pelatihan bagi disabilitas netra seperti penerapan sekuens pada sirkulasi, tata masa dan zonasi, penataan lanskap, fasade bangunan dan material yang digunakan. Tentu saja kriteria elemen formal tersebut terintegrasi dengan respon pengguna disabilitas netra (sistem perseptual) yang diwujudkan dengan desain multisensori. Pada penelitian ini diharapkan dapat berkontribusi pada pengembangan arsitektur yang inklusif dan desain universal.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.018 | 0.008 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".