Estimating the cost of regulating genome edited crops: expert judgment and overconfidence
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Experts are often called on to inform decision makers with subjective estimates of uncertain events. Their judgment serves as the basis for policy-related decision-making. This paper analyzes survey results used to collect experts' opinions of the likely cost to bring genome edited crops to market. We also examine the effect of expertise (scientific experts versus social scientists in plant biotechnology) and possible knowledge mis-calibration, both in terms of overconfidence (i.e., when subjective knowledge is inflated) and under-confidence (i.e., when subjective knowledge is deflated), on the estimation of cost involved in the development and commercial release of genome edited crops. We found that the expected costs of genome edited crops are case specific and depend on whether crops will likely be regulated as genetically modified or accepted as conventional varieties and not subject to any regulatory oversight by federal regulators. While cost evaluation of genome edited crops did not vary among scientific and social experts, it did vary among domains of knowledge. Hence, expert's performance can be described as task-specific in the context of this study.
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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 | 0.000 | 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 | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 it