Sustainable Sand Substitutes in the Construction Industry in the United States and Canada: Assessing Stakeholder Awareness
Bibliographic record
Abstract
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.
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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 | 0.002 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.005 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".