Costa Rican consumer perceptions of gene-editing
Bibliographic record
Abstract
Costa Rica's rice production, a large determinant of the country's food security, is being negatively impacted by frequently increasing periods of intense drought. Costa Rican scientists have applied CRISPR/Cas9 to develop drought resistant rice varieties they believe the country's rice producers could benefit from. However, would Costa Ricans consume gene edited rice or products derived from this crop? A three-part, 26-question survey administered in-person to 1096 Costa Ricans uncovers their attitudes, knowledge, and perceptions of gene editing technology and crops. Multiple regressions were built where the independent variables were age, gender, education level, and subjective economic situation. No statistically significant relationships were found in the regression coefficients. Moreover, the k-means procedure (cluster analysis) was used to categorize respondents according to their attitudes on the consumption of gene-edited foods: negative, neutral, and positive. Results show that overall, Costa Rican consumers are open to the application of gene editing in agriculture and would consider consuming products derived from the application of the technology. They are also open to gene editing technology being used to address human and animal health issues. However, Costa Rican consumers are not open to gene editing being used to "design" human traits. This study adds evidence to the emerging literature on the acceptance of gene-edited food. It also highlights the importance of informing societies of just how vulnerable agriculture, and therefore food security, is to the increasingly adverse effects of climate change.
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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 | 0.001 |
| 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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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 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".