Bibliographic record
Abstract
Measuring citizen activities in online environments is an important enterprise in fields as diverse as political science, informatics, and education. Over the past decade, a variety of scholars have proposed survey instruments for measuring digital citizenship. This study investigates the psychometric properties of one such measure, the Digital Citizenship Scale (DCS). While previous investigations of the DCS drew participants exclusively from single educational environments (college students, teachers), this study is the first with a survey population (n = 1820) that includes both students and the general public from multiple countries. Four research questions were addressed, two of which were focused on the validity of the DCS for this wider population. Our results suggest refining the 26-item five-factor DCS tool into an abbreviated 19-item four-factor instrument. The other two research questions investigated how gender, generation, and nationality affect DCS scores and the relationship between the different DCS factors. While gender was found to have a minimal effect on scores, nationality and age did have a medium effect on the online political activism factor. Technical skills by themselves appear to play little role in predicting online political engagement; the largest predictor of online political engagement was critical perspective and a willingness to use the Internet in active ways beyond simply consuming content.
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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 | 0.029 | 0.077 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".