Epistemic Emotions and Epistemic Cognition Predict Critical Thinking About Socio-Scientific Issues
Bibliographic record
Abstract
When thinking critically about socio-scientific issues, individuals’ expectations about the nature of knowledge and knowing, as well as their emotions when these expectations are met or not, may play an important role in critical thinking. In this study, we examined the role of epistemic emotions in mediating the effects of epistemic cognition on critical thinking when contending with conflicting information about genetically modified foods. Two hundred four university students completed a prior knowledge test on genetically modified foods, and then reported their epistemic beliefs about genetically modified foods. Participants then read a text that presented advantages and disadvantages of genetically modified foods, and reported the epistemic emotions they experienced during reading of that text. Participants then composed an argumentative essay about genetically modified foods, which were coded for critical thinking. Results from path analysis revealed that a belief in complex knowledge predicted less surprise and confusion, but more enjoyment. For the source of knowledge, a belief in the active construction of knowledge predicted less surprise and enjoyment. For justification for knowing, a belief that knowledge should be critically evaluated positively predicted curiosity, and negatively predicted confusion and boredom. Moreover, beliefs that knowledge about genetically modified foods is complex and uncertain positively predicted critical thinking. Confusion and anxiety also positively predicted critical thinking, whereas frustration negatively predicted critical thinking. Lastly, confusion mediated relations between epistemic beliefs and critical thinking. Results suggest complex relations between epistemic cognition, epistemic emotions, and critical thinking that have implications for educational practice as well as for future research on epistemic cognition and epistemic emotions.
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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 | 0.003 | 0.030 |
| 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 | 0.002 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| 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".