Can Multiple Texts Prompt Causal Thinking? The Role of Epistemic Emotions
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
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 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