The influence of instruction, prior knowledge, and values on climate change risk perception among undergraduates
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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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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 | 0.036 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.008 | 0.005 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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 it