KAJIAN IMPLEMENTASI PRINSIP DESAIN INKLUSI PADA RUANG PUBLIK, STUDI KASUS ALUN-ALUN SIDOARJO
Bibliographic record
Abstract
Alun-alun is a shared space that functions as a reception room for public. Because it is public space, alun-alun must be designed to be friendly for people with disabilities and the elderly. However, unfortunately, most public spaces in Indonesia were built without considering access for people with disabilities and the elderly. A design approach with inclusive principles was introduced as an equality-based approach where people with special needs such as people with disabilities and the elderly can explore every building and public space with a feeling of safety, comfort and confidence. The method used is a qualitative research method with a descriptive approach. The data collection technique was collected by direct observation in alun-alun Sidoarjo. The data obtained was then analyzed based on the inclusive design principles of Hawkins and friends. From the results of the study on the application of inclusive design, it can be seen that the Sidoarjo city square or alun-alun has not fully implemented the principles of inclusive design properly and correctly.
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 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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 0.001 |
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.
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".