Revisiting Social Aspects of Green Buildings: Barriers, Drivers, and Benefits
Bibliographic record
Abstract
The construction industry is recognized as one of the primary contributors to substantial environmental concerns and influences sustainable development's economic and social aspects. Green buildings have been shown a remedy to decelerate the detrimental impacts of construction on the triple bottom lines of sustainability and have therefore gained growing attention and significance in recent decades. Numerous efforts have been made to study green buildings' economic and environmental aspects. Nonetheless, not equal attention has been devoted to the social aspects of green buildings. This could be attributed to a lack of knowledge or limited understanding of social barriers, drivers, and benefits of green buildings. Failing to address and implement social aspects effectively can defeat the purpose of sustainable development in the construction industry. This study presents the preliminary outcomes of a bigger comprehensive literature review on green buildings' social barriers, drivers, and benefits. The paper intends to contribute to a more profound and thorough understanding of social sustainability in construction and can assist architects, engineers, and other construction professionals in their green buildings' decision-making processes. Presented at the Canadian Society of Civil Engineers (CSCE) 2021 Annual Conference
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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 | 0.005 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.004 |
| Science and technology studies | 0.003 | 0.006 |
| Scholarly communication | 0.007 | 0.008 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
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".