Grounding the Management of Liabilities in the Risk Analysis Framework
Bibliographic record
Abstract
Discussions of socioeconomic liability and compensation must necessarily start from an understanding of the socioeconomic, legal, and scientific basis for identifying, assessing, managing, and apportioning blame for hazards related to innovations. Public discussions about the nature of the liability challenge related to genetically modified (GM) crops and other modified organisms have focused less on direct, traditional health, public safety, technical, or environmental failures (e.g., innovations that generate hazards directly for users or indirectly to bystanders) and more on socioeconomic concerns, such as comingled product that offends product quality standards. This article examines the theoretical and legal underpinnings of the current risk analysis framework used in most Organization for Economic Cooperation and Development countries and uses it to assess two areas of significant controversy—the release of herbicide-tolerant canola and flax varieties in Western Canada. The article offers lessons for the management of liabilities arising from the introduction of GM crops.
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 | 0.060 | 0.042 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.006 | 0.002 |
| Science and technology studies | 0.005 | 0.042 |
| Scholarly communication | 0.018 | 0.018 |
| Open science | 0.007 | 0.009 |
| Research integrity | 0.010 | 0.013 |
| Insufficient payload (model declined to judge) | 0.003 | 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".