Misinformation Among Migrants: Evidence from Mexico and Colombia
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
Abstract This paper examines the effectiveness of media literacy interventions in countering misinformation among in-transit migrants in Mexico and Colombia. We conducted experiments to assess whether well-known strategies for fighting misinformation are effective for this understudied yet particularly vulnerable population. We evaluate the impact of digital media literacy tips on migrants’ ability to identify false information and their intentions to share migration-related content. We find that these interventions can effectively decrease migrants’ intentions to share misinformation. We also find suggestive evidence that asking participants to consider accuracy may inadvertently influence their sharing behavior by acting as a behavioral nudge, rather than simply eliciting their sharing intentions. Additionally, the interventions reduced trust in social media as an information source while maintaining trust in official channels. The findings suggest that incorporating digital literacy tips into official websites could be a cost-effective strategy to reduce misinformation circulation among migrant populations.
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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 | 0.002 |
| 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 | 0.002 |
| Scholarly communication | 0.000 | 0.003 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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 it