Current and Future Leaders’ Perceptions of Agricultural Biotechnology
Bibliographic record
Abstract
Were elected state FFA officers’ attitudes toward agricultural biotechnology significantly different from elected Texas legislators’ attitudes about the same topic? The purpose of this study was to determine if differences existed in agricultural biotechnology perceptions or information source preferences when compared by leadership status: current (Texas legislators) vs. future (state FFA officers). Descriptive survey methods were used to obtain data from all elected state FFA officers ( n = 360) and all House ( n = 150) and Senate ( n = 181) members in Texas ( n = 181). State FFA officers’ perceptions of agricultural biotechnology were influenced by their families’ perceptions of biotechnology; Texas legislators’ perceptions were not as affected by their families’ perceptions of biotechnology, possibly because of a function of age. Respondents perceived agricultural biotechnology as having “positive” effects on the environment. Legislators relied more on the cooperative extension service as their biotechnology information source than did state FFA officers. Both groups relied on the internet and newspapers as information sources for agricultural biotechnology. Since respondents used internet and newspapers as information sources, those working in agricultural biotechnology or those communicating it should use the internet and newspapers more often to educate leaders about agricultural biotechnology practices. Education about agricultural biotechnology through the most accessed media may produce more informed leaders, which may alleviate their concerns about agricultural biotechnology uses.
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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 | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".