Bibliographic record
Abstract
In the Chamber of Risks Understanding Risk ControversiesThrough a new set of detailed case studies, William Leiss shows that while industry and governments have made much progress in responsibly managing risks to health and environment, they remain quite poor at managing their involvements with risk issues, that is, with the often intense controversies about the way in which risks should be managed.This organizational risk, associated with misunderstanding the nature of risk management issues, can have damaging consequences, something that remains poorly appreciated.The essential problem is the failure to recognize that controversies over risks are "normal events" in modern society and as such will be with us for the foreseeable future.Three key propositions define these events: risk management decisions are inherently disputable; public perceptions of risk are legitimate and should be treated as such; the public needs to be intensively involved in the processes of risk evaluation and management.Leiss and his collaborators chronicle these organizational risks in a set of detailed case studies on genetically modified foods, cellular telephones, the notorious fuel additive mmt, pulp mill effluent, nuclear power, toxic substances legislation, tobacco, and the new type of "moral risks" associated with genetics technologies such as cloning.
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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.010 | 0.005 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.008 | 0.003 |
| Insufficient payload (model declined to judge) | 0.779 | 0.621 |
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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".