Factors affecting public attitude toward genetically modified food in Malaysia
Bibliographic record
Abstract
Public perceptions, understanding and acceptance of modern biotechnology can both promote and hamper their commercial introduction and adoption. Various studies have shown that consumer acceptance of modern biotechnology tend to be conditional and dependent on several factors. Public perceptions of biotechnology have received extensive attention in recent years in most Western countries such as Europe, USA and Canada but there have been limited similar surveys in developing countries. Most of the earlier studies used uni-dimensional or bi-dimensional instrument with multi-items or the most is four dimensions with single item. In this study, public attitude towards genetically modified (GM) soybean that is already available in the Malaysian market. A survey was carried out on 577 general public respondents in the Klang Valley region. In order to detect the structure of attitude amongst the expert group in the Klang Valley region, structural equation modeling (SEM) using AMOS version 5.1 was carried out. Result of the survey has confirmed that attitude towards complex issues such as biotechnology should be seen as multi-faceted/ multidimensional process. The most important factors predicting encouragement of GM soybean are the specific application-linked perceptions about the benefits and acceptance of risk while moral concern, risk and familiarity are significant predictors of intermediate factors. Researchers, policy makers and industries interested in developing and marketing GM products in Malaysia should consider the various factors mentioned in this in order to gain public approval.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 teacher head, 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".