Online political participation: the evolution of a concept
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
The advent of online technologies has been triggering a wave of empirical examinations of online political participation (OPP) over the past twenty years. It also stimulated scholarly debate on how to conceptualize political participation in a digital age. Scholars differ on whether to consider passive and expressive online behaviors part of or a mere precursor to political participation. This study argues that due to its rapid evolution as well as its dependence on platform affordances, quantitative empirical studies on OPP may be prone to deviations between established, much-cited definitions and measurements applied in the field. Based on a systematic literature review of 289 international peer-reviewed survey-based and experimental studies, we analyze both definitions and measurements of OPP. We find a series of disconnections: Measures preponderantly address online activities, yet merely a small share of definitions focuses on the online sphere. While only few definitions account for passive activities (e.g., reading news about politics), many operationalizations include measures capturing such passive behaviors. Expressive activities are most popular in measures of OPP, but definitions rarely reflect this focus. Finally, while measures of OPP are prone to be platform-specific, definitions tend to neglect this characteristic. We conclude by reflecting the conceptual implications of common measurement practices for the study of OPP.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it