Bibliographic record
Abstract
The question has been asked in many forums, why are major accidents still occurring? Awareness is an important basic learning factor to properly manage the lessons to be learned from major accidents. Over time, the recommendations made following an accident may be forgotten, procedures allowed to lapse, changes are made to equipment and the accident is just waiting to happen again. The memory within an organization that should help to prevent process safety accidents decays thus allowing accidents to repeat. It has been 38 years since the loss of all 84 crew members on the Ocean Ranger, the largest mobile offshore drilling unit of its day. At 1:10 a.m. EST on February 15, 1982, the Ocean Ranger’s crew sent a mayday call and abandoned the rig at 1:30 a.m. No one survived. There were no eye witnesses to tell what happened. Investigators were left with some technical evidence and the testimony from others. While the investigation report and recommendations from a Royal Commission into the Ocean Ranger tragedy changed the offshore safety regime of the time, can we learn more by re-examining the past? Can we stop learning safety by accident? Safety and environmental risk go hand in hand with industrial development. However, it is unclear whether there is a linear or nonlinear relationship between risk and industrial development. Perhaps, it is case dependent. Some industrial endeavors such as offshore development, activities in a harsher environment, or development requiring new technologies (untested and untrusted technologies) may pose a higher risk (nonlinear) than more conventional industrial development activities (e.g., petroleum refineries, petrochemical plants, pipeline transportation, and the like). Public perception plays a critical role in defining the risk versus development relationship. The public perception of risk is dependent on awareness and understanding of potential hazards and their likelihood of occurrence, and most importantly, effective communication of these along with the associated uncertainty. Public awareness can have a profound effect on the development of public policy, which in many cases is driven more by perception rather than by sound science. Two commonly used concepts of policy and decision-making will be investigated, the Precautionary Principle (PP) and As Low As Reasonably Practicable (ALARP). A clearer understanding of both approaches with an illustrative example will be provided. A process to help readers understand where and when PP versus ALARP would be most applicable is proposed.
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 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 | 0.005 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.004 | 0.010 |
| Science and technology studies | 0.005 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.006 | 0.001 |
| Research integrity | 0.001 | 0.004 |
| Insufficient payload (model declined to judge) | 0.001 | 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 teacher head, 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".