The influence of instruction, prior knowledge, and values on climate change risk perception among undergraduates
Bibliographic record
Abstract
Abstract We evaluated influences on the climate change risk perceptions of undergraduate students in an introductory Earth Science course. For this sample, domain‐specific content knowledge about climate change was a significant predictor of students' risk perception of climate change while cultural worldviews (individualism, hierarchy) and political orientation were not. These results contrast with previous studies highlighting worldviews as a dominant influence on risk perception. At the beginning of the semester, students' climate change content knowledge was relatively low, with average scores on a 21‐item test less than 50%. Post instruction results indicated that students learned climate change science during the course, and their perceptions of risks associated with climate change increased. Unlike most prior research evaluating links between climate change knowledge and risk perception, our measure of content knowledge was a validated assessment specific to climate change. Use of this specific climate knowledge test may be one reason that we detected a relationship between climate knowledge and risk perception whereas most of the previous research has not. Another—possibly complementary—explanation may be a generational shift between our study sample and prior samples. Undergraduates today, having grown up with more exposure to climate change in schools and the media than previous generations, may be diverging from average adults in that learning climate science appears to also increase their perceptions of the risks climate change poses. Undergraduate courses with embedded climate‐related activities present an opportunity to both increase climate science knowledge and risk perceptions of future decision makers.
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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 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".