Educating for a Just World: Empowering K-12 Students as Global Democratic Digital Citizens
Bibliographic record
Abstract
Given the incredible growth in online activity since the global pandemic, there is a need for an updated approach to digital citizenship education that positions students as critical designers and producers who use their learning to inform, to support, and to offer opportunities for change in their communities. In this paper, we examine some of the recent conceptualizations of digital citizenship, human rights education, and global citizenship to identify their intersections and move toward the development of a Global Democratic Digital Citizenship framework for K-12 education. We argue that current frameworks target distinct skills and competencies that enable individualistic performance of global and digital citizenship actions but neglect the development of democratic characteristics and the ways in which these are mediated by digital technologies. Students must understand how to engage respectfully with others, make their voice heard, become interculturally intelligent, and act responsibly and democratically online. Citizenship entails participation in representative democracy; therefore, citizenship education must empower youth to actively engage in local and global democratic processes through both physical and digital channels.
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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 | 0.004 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.006 | 0.008 |
| Scholarly communication | 0.017 | 0.009 |
| Open science | 0.001 | 0.015 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.006 | 0.002 |
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".