Expert and Lay Public Risk Preferences Regarding Plants with Novel Traits
Bibliographic record
Abstract
The regulation, commercialization, and adoption of a new technology involve interplay of various stakeholders, including: scientists, researchers, and developers; businesspeople in management, finance, and marketing; government agents in policy and administration; and the general public. Outcomes of these processes are therefore a function of the risk preferences of various stakeholders. Our objective is to investigate the risk preferences regarding plants with novel traits among the lay public and regulatory professionals. We investigate how framing and perceptions affect risk preferences. In particular, we test how the concept of provenance, for example, whether technology is being provided by a public university versus a multinational corporation, might affect respondents’ risk choices. We conducted a modified “Asian disease” experiment to test for Tversky and Kahneman's (1981) prospect theory, adapting the choice task to the context of new technology in agriculture. We found that framing manipulations yielded different results for both experts and laypersons: positive framing induced risk aversion more so than negative framing induced risk seeking. While framing has a strong impact on all respondents, the provenance effect is weak and inconsistent across results.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 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".