Unearthing the Construction Industry’s Awareness of and Reactions to the Global Sand Crisis
Bibliographic record
Abstract
The United Nations has declared a global sand crisis. The construction industry, as a major user of sand, needs to significantly reduce the use of this finite natural resource. The purpose of this study is to measure the level of awareness of the sand crisis among construction industry professionals, to assess their reactions upon learning of the crisis, and to determine sources of information for those who are aware of the crisis. The Cognitive-Affective-Conative model was applied as the theoretical framework. The study is based on a survey and in-depth interviews with 75 construction industry professionals in the United States and Canada. Analyses included level of awareness by professional role and by reaction, as well as level of awareness by role and by type of information. Results showed that knowledge of the sand crisis was generally low. A content analysis of interview transcripts categorized five reactions to the crisis. These results suggest that generally across all roles, those with more familiarity with the sand crisis had reactions of proactive and feel bad while those with less familiarity expressed curiosity and surprise. Denial was expressed by a few. Recommendations include: First, greater efforts are required to inform construction industry professionals of the need to reduce the use of sand. Second, it is important to determine reactions upon learning of the crisis in order to generate interest and galvanize action. Third, determining the source of knowledge can help assess effective ways of broadly disseminating information to 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 | 0.004 | 0.018 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.000 | 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".