Fostering a Total Construction Safety Culture to Enhance Safety Performance in Indonesia’s New Capital City Establishment
Bibliographic record
Abstract
Establishment of Indonesia New Capital City (Ibu Kota Nusantara) is an initiative by the Government to promote equal economic and infrastructure development throughout the country.Consequently, the report made from safety awareness reveals several unsafe conditions during the construction project, possibly arising from various factors.In general, safety culture of construction industry mirrors the evolution of 'safety culture theory'.The theory emphasizes developing beliefs and taking practical steps to reduce accidents, specifically in the construction sector.Therefore, this research aimed to validate and review the factors influencing total construction safety culture, in order to improve safety performance.Furthermore, these factors were validated using Delphi method and the results showed that 36 factors contributed to the development of total construction safety culture.This outcome helped in improving safety performance in the establishment of Indonesia New Capital City.Specifically, the 36 factors included those related to workers, management, and project complexity that could influence the development of total construction safety culture.This outcome held significant implications for improving safety performance in the establishment of Indonesia New Capital City and could be applicable in similar contexts, which can used by construction company management to create a strategy to minimize an accident.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.000 | 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".