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Record W4310525981 · doi:10.1080/15623599.2022.2146277

Awareness of the global sand crisis and sand substitutes in the construction industry in the United States and Canada: a stakeholder analysis

2022· article· en· W4310525981 on OpenAlexaboutno aff
Sheila M. Puffer, Adel A. Zadeh, Yunxin Peng

Bibliographic record

VenueInternational Journal of Construction Management · 2022
Typearticle
Languageen
FieldEngineering
TopicRecycled Aggregate Concrete Performance
Canadian institutionsnot available
Fundersnot available
KeywordsPurchasingStakeholderBusinessSustainabilityMarketingEnvironmental resource managementPublic relationsPolitical scienceEconomicsEcology

Abstract

fetched live from OpenAlex

The urgent call to address the global sand crisis has been sounded by the United Nations and environmental organizations, yet it is unclear whether this message has reached key stakeholders who are major consumers of sand and who make decisions about using sand or sustainable sand substitutes. The construction industry is by far the primary consumer of sand. Using a stakeholder framework, this study focuses on stakeholders in the US and Canadian construction industry. A survey completed by 378 respondents found very little familiarity with the sand crisis, sand substitutes in cement, or sand substitutes in building materials. No differences were found for decision makers purchasing sand and non-decision makers. As for roles, no differences were found among academics, architects, engineers, or managers except for familiarity with sand substitutes. Recommendations are offered to increase awareness of the sand crisis and encourage adoption of sustainable sand substitutes, and suggestions for further research are discussed.

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 machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.002
metaresearch head score (Gemma)0.006
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.034
Threshold uncertainty score0.244

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.006
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0020.003
Science and technology studies0.0050.002
Scholarly communication0.0030.001
Open science0.0010.002
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0020.000

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 head0.012
GPT teacher head0.226
Teacher spread0.214 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designQualitative
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

Citations6
Published2022
Admission routes1
Has abstractyes

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