Food for thought: Science communication and the public understanding of science a case study
Bibliographic record
Abstract
The objective of this research was to investigate and explain how the conversation (or lack there of) of the scientific community and the general public has shaped the course of the genetically modified (GM) food debate in Britain and Canada. In addition to providing an explanation as to how science communication has already influenced this discussion, this thesis attempts to provide insight into how to improve this discourse and by doing so, positively influence the ultimate implications of GM technology in Canada’s future. A case study approach was chosen to demonstrate the effect of science communication on public understanding in the GM food debate in Britain, as compared to Canada. The research design involved a collection and analysis of reported data in the form of a series of published opinion surveys and reports. This approach has demonstrated that science communication is the link between scientists and the public understanding of science, as it determines which scientific information is successfully communicated and which is not. It further addresses the fact that the GM food debate is not only an issue of science, but also one of social, cultural, ethical, and economic concern. The results from the case study show that the British public has tended to be more skeptical, while the Canadian public has tended to be more accepting of its government’s GM policy. Both publics expressed considerable interest in becoming involved with the biotechnology debate. There are also collateral issues, such as the underlying motives driving GM technology, which have to be considered in ultimately drawing conclusions regarding the support of biotechnology. To improve the future, scientists, governments, and the public have to acknowledge each others’ viewpoints and be able to collaborate to make viable, sustainable choices.
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 | 0.023 | 0.040 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.006 |
| Science and technology studies | 0.048 | 0.037 |
| Scholarly communication | 0.014 | 0.012 |
| Open science | 0.003 | 0.009 |
| 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".