Digital Citizenship in the Afterschool Space: Implications for Education for Sustainable Development
Bibliographic record
Abstract
Abstract Education for sustainable development (ESD) challenges traditional curricula and formal schooling in important ways. ESD requires systemic thinking, interdisciplinarity and is strengthened through the contributions of all disciplines. As with any transformative societal and technological shift, new questions arise when educators are required to venture into unchartered waters. Research has led to some interesting findings concerning digital literacies in the K-12 classroom. One finding is that a great deal of digital media learning is happening outside the traditional classroom space and is taking place in the afterschool space (Prensky, 2010). Understanding the nature of learning in the afterschool space and bridging the current divide between formal schooling and the learning happening online is critical to the establishment of core ESD values and skills, namely ethical online communities and the development of respectful, tolerant global digital citizens.
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.008 | 0.017 |
| Scholarly communication | 0.017 | 0.014 |
| Open science | 0.002 | 0.011 |
| Research integrity | 0.005 | 0.005 |
| Insufficient payload (model declined to judge) | 0.022 | 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".