Digital Rights, Digital Citizenship and Digital Literacy: What’s the Difference?
Bibliographic record
Abstract
Abstract Using digital media is complicated. Invasions of privacy, increasing dataveillance, digital-by-default commercial and civic transactions and the erosion of the democratic sphere are just some of the complex issues in modern societies. Existential questions associated with digital life challenge the individual to come to terms with who they are, as well as their social interactions and realities. In this article, we identify three contemporary normative responses to these complex issues –digital citizenship, digital rights and digital literacy. These three terms capture epistemological and ontological frames that theorise and enact (both in policy and everyday social interactions) how individuals learn to live in digitally mediated societies. The article explores the effectiveness of each in addressing the philosophical, ethical and practical issues raised by datafication, and the limitations of human agency as an overarching goal within these responses. We examine how each response addresses challenges in policy, everyday social life and political rhetoric, tracing the fluctuating uses of these terms and their address to different stakeholders. The article concludes with a series of conceptual and practical ‘action points’ that might optimise these responses to the benefit of the individual and society.
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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 | 0.012 | 0.024 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.005 | 0.053 |
| Scholarly communication | 0.022 | 0.028 |
| Open science | 0.001 | 0.007 |
| Research integrity | 0.007 | 0.008 |
| Insufficient payload (model declined to judge) | 0.007 | 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".