Youth Awareness and Expectations about GMOs and Nuclear Power Technologies within the North American Free Trade Bloc: A Retrospective Cross-Country Comparative Analysis
Bibliographic record
Abstract
This study reports on the cross-country heterogeneity in youth awareness and expectations about genetically modified organisms (GMOs) and nuclear power technology (NPT) within the North American free trade area (NAFTA). Models are estimated with data on youth respondents from the USA, Canada and Mexico, using seemingly unrelated bivariate weighted ordered probit regression, with maximum simulated likelihood estimation. Our findings show that the diffusion of technology and information within the trade bloc, for the 20 years prior to the 2015 data collection period, did not significantly contribute to cross-country convergence in youth awareness and expectations about GMOs and NPTs. Indeed, with regard to awareness, compared to youth from the USA, those from Canada show 15% (GMOs) and 7.1% (NPT) more awareness, respectively; while youth from Mexico show 34.4% and 19.5% less awareness about GMOs and NPT, respectively. With respect to expectations about future developments of the two technological artifacts, compared to youth from the USA, those from Canada and Mexico are 34.4% and 39.9% more optimistic about GMOs, respectively, while 15% and 49.7% are more optimistic about NPT. Overall, our findings show that the youth population within NAFTA is 2.5% and 6.7% more optimistic about GMOs and NPT for each level of increase in their awareness about the two technologies, respectively. Theoretically, our results seem to reject the hypothesis of NAFTA being a technology convergence country club in the Schumpeterian view, while seemingly supporting the existence of heterogeneous growth regimes within NAFTA.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".