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Record W3209504255 · doi:10.11606/gtp.v16i4.178511

Critical analysis of housing condition impacts on residents' well-being and social costs

2021· article· en· W3209504255 on OpenAlexfundno aff
Elisa Atália Daniel Muianga, Vanessa Gomes da Silva, Dóris Catharine Cornelie Knatz Kowaltowski, Daniel de Carvalho Moreira, Ariovaldo Denis Granja, Carolina Asensio Oliva, Ruth Ferreira da Silva

Bibliographic record

VenueGestão & Tecnologia de Projetos · 2021
Typearticle
Languageen
FieldEconomics, Econometrics and Finance
TopicHousing Market and Economics
Canadian institutionsnot available
FundersEconomic and Social Research CouncilConselho Nacional de Desenvolvimento Científico e TecnológicoFundação de Amparo à Pesquisa do Estado de São PauloInternational Development Research Centre
KeywordsQuality of life (healthcare)BusinessSocial WelfareWelfarePublic economicsMental healthPsychologyPolitical scienceEconomics

Abstract

fetched live from OpenAlex

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.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.208
Threshold uncertainty score0.775

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.021
GPT teacher head0.265
Teacher spread0.244 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations12
Published2021
Admission routes1
Has abstractyes

Explore more

Same venueGestão & Tecnologia de ProjetosSame topicHousing Market and EconomicsFrench-language works237,207