Improving Communication in Agbiotech Projects: Moving Toward a Trust-centered Paradigm
Bibliographic record
Abstract
Communication with end users about agricultural biotechnology does not necessarily lead to commensurate adoption of biotech crops. Agbiotech communication implies challenges like disagreement between proponents and opponents of genetically modified (GM) technology and media influence on public opinion, both of which can negatively impact public trust in, and thus adoption of, biotech crops. We argue that communication strategies for introducing biotech crops should focus on building and fostering trust between project partners developing biotech crops and the community they intend to serve to facilitate effective adoption of the crops. Strategies should include a combination of knowledge dissemination; early and continuous communication; provision of training; emphasis on end-user benefits; and transparency about agbiotech projects – all with the aim of building and fostering trust between partners of agbiotech projects and the community.
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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 | 0.100 | 0.157 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.012 | 0.020 |
| Scholarly communication | 0.023 | 0.035 |
| Open science | 0.004 | 0.022 |
| Research integrity | 0.014 | 0.012 |
| Insufficient payload (model declined to judge) | 0.006 | 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".