Universal and Inclusive Design in Public Open Spaces for Wellbeing-Oriented Cities: Design Strategies for the Case of Alexandria Public Beach
Bibliographic record
Abstract
Globally, designing cities to meet diverse community needs and improve well-being is becoming increasingly essential and challenging.At the foremost of this challenge is inclusive design (ID), which considers all individuals, is a key aspect, coinciding with a growing focus on public open spaces (POS), particularly after the outbreak of COVID-19.The pandemic and its aftermath led people to use POS to fulfill physical and recreational activities, revealing a lack of ID understanding for diverse abilities.Thus, there is a need to consider different design aspects of POS such as ease of accessibility, inclusiveness, and social interaction to maintain a wellbeing-oriented city design.From the previous, this paper aims to understand ID in POS and address design aspects in order to make cities more resilient under the mandate of Goal 11 of the sustainable development goals (SDGs).The paper follows three main phases: theoretical, identifying the research problem and framework; analytical, reviewing existing literature on universal and inclusive design (UID) strategies in POS; and empirical, analyzing a disability-friendly public beach in Alexandria as a case.The aim is to bridge the gap between design and user needs, proposing strategies and recommendations for future urban interventions.
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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 | 0.003 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.009 | 0.009 |
| Scholarly communication | 0.007 | 0.004 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".