Managing natural disaster risk through enforcement of development standards
Bibliographic record
Abstract
Severe earthquakes in urban areas can cause great damage to the built environment. In urban areas, the built environment consists of infrastructure systems such as highways and bridges, commercial complexes, and residential buildings. As pointed out in the literature, enforcement of building codes is not well carried out. It is estimated that better building code compliance and enforcement could have prevented 25% of the insured losses from Hurricane Andrew, which hit Southern Florida just south of Miami in August, 1992. Therefore, it is important to ensure that infrastructure and residential development projects meet the prescribed standards. Consequently, factors affecting compliance to building codes and standards are investigated. Firstly, inspection and enforcement processes used in major infrastructure and residential development projects are described. Secondly, the "command-and-control" approach to building safety is studied by utilizing a game-theoretic model. The model is expressed as a game in extensive form in which the two decision makers are the developer, who builds a development project and is potentially motivated to violate the building standard, and the inspector, representing the government agency which inspects and enforces the standard in question. Parameters considered in assessing the cost-effectiveness of building code enforcement are the gains for violators, the costs of inspection, penalties, and the social value for stopping violations. Implications for disaster risk management are presented.
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 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 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".