Citizens as consumers: styles of reasoning about agricultural biotechnologies and publics
Bibliographic record
Abstract
In research and policy there is a dominant style of reasoning about the contribution agricultural biotechnologies can make to resolving major global challenges. In this reasoning, consumer scepticism is a major hindrance to deploying biotechnology and there is a significant focus on understanding consumer opinion in order to manipulate it. Analysing the historical role given to publics in public opinion research, and within technology research and policy, demonstrates that the framing of publics is largley shaped by economics. A review of academic publications on agricultural biotechnology and publics between 1995 and 2021 reveals some of the core tenets of this style of reasoning. The dominant framing of publics as individual consumers confines attention to concerns with end products on supermarket shelves. Theories and methods are focused on understanding individual perceptions, and fixed response questions reify the expert/public divide. This obscures broader public concerns with agricultural biotechnologies, such as issues of social justice or governance of uncertainty. A broader framing of different publics and their opinions of technology development and deployment would improve understanding of the issues that concern people as citizens, and enable more meaningful public engagement with agricultural biotechnologies.
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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 | 0.052 | 0.065 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.005 |
| Science and technology studies | 0.008 | 0.054 |
| Scholarly communication | 0.026 | 0.038 |
| Open science | 0.003 | 0.010 |
| Research integrity | 0.011 | 0.008 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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".