Non-Participation in Digital Media: Toward a Framework of Mediated Political Action
Bibliographic record
Abstract
This article explores the notion of digital non-participation as a form of mediated political action rather than as mere passivity. We generally conceive of participation in a positive sense, as a means for empowerment and a condition for democracy. However, participation is not the only way to achieve political goals in the digital sphere and can be hampered by the ‘dark sides’ of participatory media, such as surveillance or disempowering forms of interaction. In fact, practices aimed at abandoning or blocking participatory platforms can be seen as politically significant and relevant. We propose here to conceptualize these activities by developing a framework that includes both participation and non-participation. Focusing on the political dimensions of digital practices, we draw four categories: active participation, passive participation, active non-participation, and passive non-participation. This is not intended as a conclusive classification, but rather as a conceptual tool to understand the relational nature of participation and non-participation through digital media. The evolution of the technologies and practices that compose the digital sphere forces us to reconsider the concept of political participation itself.
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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 | 0.008 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.004 |
| Science and technology studies | 0.005 | 0.038 |
| Scholarly communication | 0.019 | 0.015 |
| Open science | 0.003 | 0.007 |
| Research integrity | 0.005 | 0.005 |
| Insufficient payload (model declined to judge) | 0.005 | 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".