Bibliographic record
Abstract
Conversations in middle school about digital citizenship tend to focus on the responsibilities of citizenship and the issues of surveillance, safety, cyberbullying, and internet etiquette. While these are important and essential conversations, digital citizenship education needs to consider youth political identity and democratic participation in digital spaces if educators wish to take full advantage of the empowering potential of participatory technology. The potential for youth to shape diverse identities through digital technologies has significant implications for youth empowerment and agency and helps dismantle reductive narratives that have tended to define middle school youth. The role of digital citizenship education must be expanded to include critical social justice education. Such a reconceptualization of digital citizenship will result in curriculum that understands and supports the role digital technologies play in the development of youth political identity and help empower young people to impact positively on political issues. Little research has been done on the convergence of youth political identity and participatory technology spaces which are designed specifically for social justice and supported by social justice pedagogical ideals. Online social justice spaces support user empowerment through critical social justice education, community building, and orientation toward social action. If the context of youth lived experience is a technological one, the expression of youth political identity and youth activism through digital pathways requires attention and support from educators interested in digital citizenship 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 | 0.003 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.002 | 0.007 |
| Scholarly communication | 0.007 | 0.013 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.009 | 0.001 |
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".