Playing With Your Emotions
Bibliographic record
Abstract
As a result of the COVID-19 pandemic, online misinformation has proliferated, requiring innovative interventions to improve information literacy for the public. In this paper we report on an evaluation of an interactive narrative education intervention developed as part of a design-based research project into microlearning and COVID-19 misinformation. The intervention aimed to enable learners to (1) name the role that fear and anger play in the spread of misinformation, and (2) identify a strategy for interrupting the spread of misinformation driven by fear or anger. Using a pre- and post-test design, we surveyed 195 Canadian women to evaluate whether the intervention was effective at achieving its learning outcomes. Results indicate that the intervention was effective in improving understanding about emotionally-driven misinformation, the role of emotions in the circulation of misinformation, and self-efficacy with respect to this type of misinformation. Based on these results, we suggest that short interactive narratives may be useful in education efforts aimed to address online misinformation and that this technique may have wide appeal to educators seeking accessible web tools for teaching this type of content.
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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 | 0.001 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.003 | 0.004 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.015 | 0.006 |
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".