Critical analysis of housing condition impacts on residents' well-being and social costs
Bibliographic record
Abstract
Housing is fundamental to the welfare of people and society. On the contrary, housing may impose costs on users as regards their health and quality of life. These costs are not only individual but also social. Studies on the concept of social costs (SCs) related to living conditions of social housing (SH) are scarce, and the concept needs in-depth debates. A systematic literature review (SLR) was conducted to answer the primary research question: What are SCs, and what triggers them? The research specifically aims to identify spatial design factors and construction details of SH, which may cause adverse impacts and social costs and affect households' quality of life. The SLR results are analysed and discussed concerning the major concepts of SCs and social impacts (SIs). The visual representation and organization of data contribute to detailed and in-depth conceptual discussions to understand the factors that can induce actions to improve SH design and upgrading of the existing stock. Most publications emphasise physical and mental health risks. Poor thermal conditions cause illnesses, and depression is prevalent in many housing developments putting pressure on public systems and their health services. Social unrest and family conflict can impose further costs on policing and social assistance. Housing conditions’ cause and effect are rarely detailed in the SC literature, which constitutes a research gap. New housing design, Upgrading or refurbishment initiatives should also effectively increase well-being, reduce environmental impacts, and ultimately contribute towards positive social and technological developments.
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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 | 0.015 | 0.046 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.013 | 0.008 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 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".