The public opinion climate for gene technologies in Canada and the United States: competing voices, contrasting frames
Bibliographic record
Abstract
This exploratory study of Canadian and US public opinion about gene technologies is based primarily on survey data collected by the Government of Canada, with media data from a widely available commercial database (LexisNexis) used in an illustrative case study of the apparent resonance between the climate of opinion and media frames in different regions of the two countries. The study uses regression modeling, factor analysis and cluster analysis to characterize the structure of the opinion data, concluding that observed opinion differences might be understood in terms of the greater number of individuals in the United States who belong to an identifiable opinion group that believes these technologies are benign and must be developed (termed, for convenience, “true believers”), as well as a somewhat greater number in Canada who belong to a group believing that ordinary people should be able to decide based on ethical considerations (“ethical populists”). However, the most common group in each country is made up of people who believe risks or costs and benefits should be weighed in developing policy, and that this should be done by experts (“utilitarians”). This group and two other cluster groups identified in the analysis (“moral authoritarians” and “democratic pragmatists”) exist in roughly equivalent proportions in both countries, with some regional variation evident within each. While these observations represent descriptive findings only, they nevertheless underscore the complexity of the opinion climate and problematize the development of consensus policy. Preliminary analysis of news coverage of selected gene technologies revealed both similarities and differences in patterns of news discourse between Canada and the US. A sample of stem cell coverage for February 2004, following the American Association for the Advancement of Science meeting in Seattle (during which the announcement of new Korean research on human cloning was made), was used as a case study for a pilot media analysis.
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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 | 0.009 | 0.021 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.006 | 0.009 |
| Science and technology studies | 0.017 | 0.009 |
| Scholarly communication | 0.010 | 0.002 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.003 |
| 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".