Thinking beyond green space: exploring a development project for aligning with UN SDGs by incorporating social value in architecture
Bibliographic record
Abstract
Traditional theories, approaches, and models for architecture and urban design mostly considered formal outlook and functional efficiency. During the mid-20th century, after industrialization, public spaces lost their quality and identity because of too much focus on efficient transportation. Recently, since the latter part of the 20th century, the shifting of attention to another dimension of architecture, environmental sustainability, has initiated various technological innovations and paradoxes. This has produced some well-designed architecture and green spaces, but professionals have mostly regarded those extraordinary situations as segregated parts of the city. Consequently, communities have either transformed or rejected them. Despite the introduction of the United Nations Sustainable Development Goals (UN SDGs) in 2015, architects and developers are still designing buildings that are environmentally sustainable and economically profitable only where focuses are limited to— maximizing renewable energy, waste reduction; and minimizing carbon footprint, resource consumption. However, these negligences in solving social segregation; disregard for autonomy; lack of possibilities for participation; and ignorance of equity are deteriorating both the physical and psychological well-being of residents. In addition, failing to consider the ‘green space’ as an inclusive ‘public space’ is alienating them. Though authorities often claim the goal is to provide a solution that— benefits residents and investors; and positively contributes to key sustainability indicators. Therefore, the abovementioned shreds of evidence indicate that the current development projects often lack social value, which also often bottlenecks the overall sustainability. Considering these issues, this study quest for “How can social value in architecture be implemented and integrated into real-life projects to contextualize and achieve SDGs?” Therefore, this research objects to – firstly, explore ‘social value’ as a tool for ensuring social sustainability and achieving SDGs; secondly, identify different stakeholders of a development project; and finally, specify various activities and aspects of stakeholders for aligning with SDGs. By adopting a case study-based approach, this qualitative research collected data through site visits, Key Informant interviews (KII), semi-structured interviews, questionnaire surveys, and observation. Based on the empirical findings, this paper proposes a list of stakeholders and activities according to people’s preferences, aligned to achieve SDG 10 and SDG 11, for creating an inclusive, sustainable, and socially connected public space.
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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 | 0.008 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.005 | 0.009 |
| Scholarly communication | 0.010 | 0.010 |
| Open science | 0.002 | 0.011 |
| Research integrity | 0.003 | 0.004 |
| 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".