Design Strategies for Inclusive Public Space to Facilitate Street Vendors: A Case Study of Jetayu Park, Indonesia
Bibliographic record
Abstract
Street vendors (SVs) play a vital social and economic role in urban life, particularly in cities of the Global South.During the COVID-19 pandemic, their role in sustaining community economies became more evident.However, SVs are often overlooked in urban planning, especially in the design of inclusive public spaces.This study aims to formulate inclusive open space design strategies that support informal sector activities as part of a broader commitment to social inclusion.Jetayu Park in Pekalongan, Indonesia, is used as a case study to explore the spatial integration of SVs in a multifunctional public park.A mixed-methods approach was employed, combining quantitative data from questionnaires (n=54) with qualitative spatial analysis.The findings indicate that SVs require spaces that are responsive to operational time, thermal and visual comfort, physical and visual accessibility, safety, spatial flexibility, and adequate supporting infrastructure.Based on inclusive design principles, the study proposes five key strategy categories: (1) Enhancing Temporal and Operational Adaptability, (2) Providing Environmental Comfort, (3) Allocating Strategically Accessible Locations, (4) Ensuring Mobility and Safety, and (5) Implementing Technical Considerations for Inclusive Design.These strategies are contextualized to Jetayu Park but offer adaptable guidance for other cities in the Global South.By integrating SVs into the urban design process, this study contributes to strengthening the framework for inclusive public space-where formal and informal users can coexist and thrive.The proposed framework demonstrates how inclusive design can support equity, flexibility, and multifunctionality in public space development.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.010 | 0.003 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".