Old concept with new power: Why digital and unconventional activities can be political participation
Bibliographic record
Abstract
Digitally networked activities and unconventional actions on a community level increase citizens’ repertoire of participation. Political communication research, however, is having a hard time to consistently integrate such activities into the concept of political participation. Our study empirically tests how activities like crowdfunding or urban gardening can be combined with more traditional forms of participation. We use 34 participatory activities from a national survey conducted in Denmark (N=9125) and apply a suggested framework of political participation by van Deth (2014) to this selection. A confirmatory factor analysis demonstrates the existence of four distinct types of political participation. We show that activities formerly described as civic engagement (Zukin et al., 2006; Norris, 2002) can be re-integrated into the concept of political participation and confirm that digitally networked activities rather than being an unidimentional construct are an integral part of most types of political participation. With the suggested measures for these four types of political participation, we give a starting point for future empirical studies to apply this more comprehensive and timely conceptualization of political participation.
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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 | 0.009 | 0.022 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.004 | 0.045 |
| Scholarly communication | 0.012 | 0.020 |
| Open science | 0.002 | 0.009 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.011 | 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".