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Record W4283385942 · doi:10.3390/su14137674

Sustainable Sand Substitutes in the Construction Industry in the United States and Canada: Assessing Stakeholder Awareness

2022· article· en· W4283385942 on OpenAlex

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.
aboutThe title or abstract carries a Canadian signal from the geographic lexicon.

Bibliographic record

VenueSustainability · 2022
Typearticle
Languageen
FieldEngineering
TopicRecycled Aggregate Concrete Performance
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsStakeholderBusinessConstruction industrySustainable developmentConsumption (sociology)Stakeholder theoryMarketingEnvironmental resource managementPublic relationsPolitical scienceEngineeringEconomicsSociologyConstruction engineering

Abstract

fetched live from OpenAlex

The United Nations has declared a global sand crisis, called for reduced sand consumption, and proposed solutions to address the crisis, including adopting sustainable substitutes for sand. The construction industry is a major consumer of sand, yet a recent study found a very low level of awareness by stakeholders of the crisis. The purpose of this study is to assess the familiarity of construction industry stakeholders with 27 sand substitute materials, grouped into five components that emerged from a factor analysis. Data were collected using a survey designed by the authors. Respondents consisted of 156 construction industry professionals located in 35 US states and 7 Canadian provinces. Stakeholders were classified according to a framework considering the level of power and interest of each stakeholder in sustainable construction projects. Hypotheses of no differences in awareness for two types of stakeholder groups were generally supported. First, no differences were found for decision makers responsible for ordering sand vs. non-decision makers. Second, for professional roles, academics were more familiar with some substitutes than those in other roles. The article concludes with implications for research and practice, with recommendations on how to increase awareness of sand substitutes among stakeholders in the construction industry.

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)0.001
metaresearch head score (Gemma)0.000
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 score0.419
Threshold uncertainty score0.896

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
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 head0.015
GPT teacher head0.233
Teacher spread0.218 · 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