Identifying Actions to Control and Mitigate the Effects of the COVID-19 Pandemic on Construction Organizations: Preliminary Findings
Why this work is in the frame
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Bibliographic record
Abstract
Construction organizations face many challenges in coping with the effects of the COVID-19 pandemic on the economy and the construction work environment. Although each organization is implementing its own strategies to mitigate this pandemic’s impact on operations and workers, the construction industry as a whole urgently needs to identify the most effective strategies for mitigating the effects of COVID-19 on its operations in the short term while preparing response plans and long-term recovery plans. This article presents preliminary findings of a survey conducted with construction organizations primarily in North America to identify and assess mitigation actions taken. Recommendations based on these findings are provided to help construction organizations during this pandemic. The results of this study will help in developing evidence-based operational strategies to identify new modes of operating for construction organizations during both the current pandemic and any future pandemics.
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Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.005 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it