Can Multiple Texts Prompt Causal Thinking? The Role of Epistemic Emotions
Bibliographic record
Abstract
When individuals seek to learn about scientific information, they likely turn to the Internet. There, they will find multiple documents with conflicting points of view and varying degrees of accuracy. Integrating this information is challenging and may evoke epistemic emotions which may, in turn, influence how this information is integrated. Additionally, understanding complex scientific topics such as climate change requires causal reasoning. The current study investigated the role of emotions and prior knowledge in learning about the causes and effects of climate change from multiple texts. One hundred and twelve university students read either a congruent argument (two texts affirming the same point of view) or an incongruent argument (two texts with competing points of view). Text presentations were counterbalanced. Those who read congruent texts showed greater knowledge gains and were more likely to think causally than those in the incongruent group. Across all conditions, emotions tended to decrease in salience as participants read the second text, suggesting that individuals may become desensitized to the challenges of climate change with increased exposure to information. This suggests that caution must be taken to avoid promoting disengagement and inaction of individuals around controversial issues.
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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 | 0.004 | 0.059 |
| 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 | 0.003 |
| Scholarly communication | 0.004 | 0.005 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 0.001 |
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".