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Record W4387457207 · doi:10.3390/su151914569

Social Life-Cycle Assessment in the Construction Industry: A Review of Characteristics, Limitations, and Challenges of S-LCA through Case Studies

2023· review· en· W4387457207 on OpenAlex
Prisca Ayassamy, Robert Pellerin

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueSustainability · 2023
Typereview
Languageen
FieldEngineering
TopicSustainable Building Design and Assessment
Canadian institutionsPolytechnique Montréal
Fundersnot available
KeywordsOperationalizationScope (computer science)Life-cycle assessmentSocial impact assessmentUnit (ring theory)Systematic reviewManagement scienceRisk analysis (engineering)Process managementEngineeringBusinessComputer sciencePsychologyEnvironmental planningPolitical scienceMEDLINEEconomicsEnvironmental science

Abstract

fetched live from OpenAlex

The paper aims to examine how researchers have operationalized social impact assessment in construction projects over the last ten years. A systematic review was used to investigate case studies in the Social Life-Cycle Assessment (S-LCA) to analyze the application of the methodology. In total, 19 articles published between 2012 and 2023 were classified according to their scope, functional unit measure, S-LCA indicators used, and the main challenges. Our findings revealed limitations in both qualitative and quantitative aspects of measuring social indicators, primarily stemming from difficulties associated with scoring and assessment methodologies. Additionally, we observed deficiencies in social data within the S-LCA framework. This suggests that potential social impacts may be inadequately addressed and evaluated due to various challenges that have been highlighted in the existing literature.

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.

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)0.002
metaresearch head score (Gemma)0.002
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Systematic review · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.579
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0020.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
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 head0.151
GPT teacher head0.411
Teacher spread0.261 · 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