The Disinformation Link: A Discussion of How Global Citizenship Education Contributes to Global Political Stability
Bibliographic record
Abstract
The threat of Social Polarization and disinformation is growing in these post-pandemic times, threatening the political stability and social cohesion of the world, and education has become an option to address this crisis. This paper examines Global Citizenship Education (GCE)’s ability to counteract disinformation and Social Polarization (SP) using media and literacy training (MIL), as previous studies on these topics rarely address their direct relationships. The finding suggests that there is a strong probability that MIL, under the guidance of the GCE framework, has the capability to mitigate disinformation and, in turn, SP. However, due to limited evidence in the area, this paper is unable to confirm the relationship between SP and Global Political Stability. Despite this, this paper still offers a unique perspective on GCE, which helps set the stage for future studies to explore related topics. The insights derived from this paper are valuable for various actors and researchers, which could help contribute to a more stable, peaceful global society.
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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 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.008 | 0.023 |
| Scholarly communication | 0.011 | 0.012 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.006 | 0.007 |
| Insufficient payload (model declined to judge) | 0.008 | 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".