Empowering Youth to Combat Malicious Deepfakes and Disinformation: An Experiential and Reflective Learning Experience Informed by Personal Construct Theory
Bibliographic record
Abstract
The potential to weaponize deepfakes is growing at an alarming rate. The study aimed to explore how education can help youth develop resilience to malicious deepfakes and the ability to counter disinformation, regardless of context. Sixteen youth between the ages of 18 and 24 participated in a 9-h, cutting-edge, experiential, and reflective learning experience on deepfakes and disinformation informed by personal construct theory (PCT). Participants experienced the creation of deepfakes and assessed their ability to counter disinformation. They delved into their own construct systems and reflected on the genesis of their vulnerabilities. They moved from being unfamiliar with the deepfake phenomenon to becoming empowered digital citizens, motivated to develop their skills in assessing the validity of online information and resisting manipulation regardless of its source. The study provides recommendations for more targeted education about deepfakes and disinformation for youth. Educators, curriculum developers, and policymakers can use these findings to ensure that a well-equipped generation of digital citizens protects society from the growing disinformation plague. With this proof of concept, the next step is to bring this approach to a larger number of youth and contribute to the fight against malicious deepfakes, while developing strategies to integrate PCT-informed learning experiences into education.
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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 | 0.006 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.003 | 0.005 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.002 | 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 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".