Project Level Targeting of Occupational Risk Areas for Construction Workers Using OSHA Accident Investigation Reports
Bibliographic record
Abstract
The occupational health and safety information in the United States are recorded using a standard classification system defined by the government regulations. For over sixty years, the Standard Industrial Classification (SIC) system has served as the structure for the collection and analysis of the occupational health and safety data. In 2004, this system was replaced by the North American Industry Classification System (NAICS) which was developed in cooperation with Canada and Mexico. Although the new classification system includes additional sectors, both code definitions are primarily based on the main industry sectors and the types of activities performed. This approach provides information for identifying the high risk activities; however, it does not present any project level information. The type of the project and related circumstances make a significant difference for the level of risk exposure and severity of the injuries. This paper presents an effort to identify the type of construction projects from the existing SIC/NAICS based occupational safety data using Occupational Safety and Health Administration accident reports. An analysis of the fatal accident reports from 1999 to 2002 coded under “electrical work ” (SIC 1731) is included as an illustration case.
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 | 0.003 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.010 | 0.006 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".