Teacher education and digital citizenship: Bridging classrooms, communities and digital realms
Bibliographic record
Abstract
To be active citizens in today’s media-saturated world, youth need to use, critique, and create digital media; yet, there is still relatively little experimental research on new pedagogical approaches to support urban youth as future digital citizens. The introduction of new technologies into the classroom continues to be a challenge for educators, especially when concerned about developing active citizenship among urban youth, many of whom are newcomers to Canada. We argue that urban youth would greatly benefit from innovative inquiry-based pedagogies that afford them opportunities to connect to local, national, and global communities from their classroom as digital citizens. This symposium will use emerging findings from a SSHRC-funded study to consider some innovative practices for reconceptualizing and developing teacher candidates’ knowledge of digital literacies, civics, and citizenship education in order to respond to such 21 st century urban contexts.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.009 | 0.021 |
| Scholarly communication | 0.014 | 0.011 |
| Open science | 0.001 | 0.014 |
| Research integrity | 0.002 | 0.003 |
| 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".