Selected Paper Joint 2004 Northeast Agricultural and Resource Economics Association and Canadian Agricultural Economics Society Annual Meeting
Bibliographic record
Abstract
The study applies multivariate statistical and econometric tools to estimate the importance of the various factors driving Korean consumer acceptance of GM food products. The evidence thus far on biotechnology is decidedly mixed: public perceptions of food biotechnology are characterized by ongoing tension between opposing forces. The south Korean perceptions about food in general and ranges from excitement about the promise of environmental and economic benefits from GM products to fear and distrust of the technology for unknown risks. This highlights the importance of credibility of private and public institutions responsible for certifying the safety of GM foods and implementing necessary regulatory controls on GM processes and products. In between, many people are undecided, trying to learn more about the issues and reach a definitive position. Encouraging though is that some people are eager to try new foods. Koreans strongly favors food naturalness, familiarity, and access just as the west countries. Results suggest that the South Korean Consumer priorities with respect to various biotechnology and general food issues are related to their socioeconomic and value attributes. This implies that, at least in the near term, there will be considerable divergence within the
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.005 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.157 | 0.021 |
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".