It’s Contagious! The Effects of Gamifying Refutation Texts on Emotions and Learning
Bibliographic record
Abstract
The current study investigated the effects of gamifying refutations on emotions and learning. Refutations have a substantial body of evidence supporting their use to correct misconceptions, yet reduced efficacy has been observed for some topics that induce negative emotional reactions. We tested whether gamification could mitigate these limits given that it capitalizes on positive affective engagement. From May to December 2020, approximately 200,000 individuals were recruited from social media in Canada to engage with a non-game interactive survey as a control or a fully gamified platform focused on correcting COVID-19 misconceptions. Gamification resulted in higher levels of happiness and anxiety and lower levels of anger and skepticism in response to having misconceptions corrected by refutations. Further, participants who engaged with gamified refutations retained correct information after a brief period. Finally, positive emotions and anxiety positively predicted and negative emotions largely negatively predicted retention and support for related public health policies. Implications for scaling up and reinforcing the benefits of refutations for public engagement with science are discussed.
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.
How this classification was reachedexpand
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 | 0.000 | 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 | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".