How Seventh-Grade Students Experience the Complexity of Socioscientific Issues Through Decision Making on the Autonomous Vehicle Issue
Why this work is in the frame
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Bibliographic record
Abstract
Abstract This study investigates what perspectives younger students considered and how they experienced the complexity of multiple perspectives about autonomous vehicle issues. Over the course of 6 weeks, 28 seventh-grade Korean students participated in role-play and group discussion to understand different perspectives on the issue. We qualitatively analyzed students’ positions toward these issues before and after the class and their perspectives in group decision making. The results indicate that students showed anxiety toward artificial intelligence systems, thus opposing it. They also explained where their concerns about the new technology arose to justify their views and opposition. We also found different patterns when students experienced uneasiness and conflicts in a group decision-making process. The patterns can be classified as (1) exploring multiple perspectives for decision making and (2) experiencing conflicts in working toward group consensus. Implementations for incorporating diverse perspectives into teaching strategies are discussed.
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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 | 0.006 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.004 |
| Science and technology studies | 0.010 | 0.007 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.004 | 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 it