Conceptualizing and contextualizing digital citizenship in urban schools: Civic engagement, teacher education, and the placelessness of digital technologies
Bibliographic record
Abstract
In September 2014, pro-democracy demonstrators in Hong Kong mobilized to bypass online government censorships, connecting through their Smartphones using the FireChat app. In 2013, four Saskatchewan women used Facebook chat to speak out against the proposed Federal Bill-45, initiating the IdleNoMore movement. In each of these cases, digital technologies were used to bypass the “official” channels of civic engagement. In this way, digital technologies can provide spaces within which non-dominant social groups can network around – and mobilize against – the entrenched interests embedded in traditional media. At the same time, however, digital technologies can become obstacles to civic engagement. In the 2016 US election, for example, Facebook was at the centre of controversies over fake news and “digital echo chambers.” As citizenship educators, therefore how can we engage with digital technologies in a positive way, in order to create decentred spaces for civic engagement within the diversity of 21 st century classrooms? In what follows, we first review existing research within the scholarly and policy contexts of civic engagement in urban schools and 21 st century learning skills. We then present the conceptualization of digital citizenship that guides our project, with particular emphasis on the different spaces in which urban youth can be (and are) civically engaged. Finally, we discuss the context of our project, present some initial findings, and reflect on some of the obstacles we have encountered so far. In particular, we discuss our attempt to develop faculty/school partnership model as a way making the curriculum more locally relevant and meaningful to learners.
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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 | 0.005 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.005 | 0.004 |
| Science and technology studies | 0.014 | 0.057 |
| Scholarly communication | 0.019 | 0.016 |
| Open science | 0.002 | 0.018 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.003 | 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".