Principles for Incorporating Farmers in the Ethical Assessment of Genetically Modified Crops
Bibliographic record
Abstract
A current advance within the agricultural industry is the use of genetic engineering to produce novel crops for food production.This technology raises questions about how societies should position themselves with respect to genetically modified (GM) crop development and implementation; namely, how should the potentials and risks of this technology be evaluated?We argue that current methods to evaluate the risks and benefits of GM crops are inadequate and not conducive to the strategic development of this technology, where a way to ameliorate technology assessments for GM crops is to include farmers in the research process of evaluating these crops prior to their commercialization.However, particularities concerning the ethical status of such research require special consideration and vigilance.For example, in such technology assessment initiatives, farmers would occupy both the roles of research participant and research investigator.Other particularities surface due to factors related to the nature of GM crops.These particularities are examined with reference to concepts drawn from the field of research ethics, namely informed consent, compensatory decisions, and issues of participant inclusion/exclusion.
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 | 0.351 | 0.258 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.015 | 0.081 |
| Scholarly communication | 0.018 | 0.020 |
| Open science | 0.006 | 0.016 |
| Research integrity | 0.044 | 0.041 |
| Insufficient payload (model declined to judge) | 0.002 | 0.002 |
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".