Resisting and Claiming Digital Sovereignty: The Cases of Civil Society and Indigenous Groups
Bibliographic record
Abstract
ABSTRACT In this contribution to “Unthinking digital sovereignty,” we review some discourses and practical proposals that resist or challenge the digital sovereignty of “established states.” Our focus centers on two distinct sociopolitical groups engaged in such practices. The first group we highlight is what we call “civil society,” which is composed of nongovernmental, nonprofit organizations, and collectives, often inspired by anti‐capitalist, autonomist, and anarchist ideas, actively involved in building self‐managed digital infrastructures based on free and open‐source software and hardware. The second group consists of Indigenous peoples and organizations that have developed a robust discourse on “data sovereignty,” referring to the sovereignty of knowledge and data related to them. Finally, we discuss four contributions from our review that help “unthink” digital sovereignty: (1) the values of autonomy and self‐determination, which are quite important beyond state‐centric models; (2) the cultural dimensions of digital sovereignty; (3) the question of “openness,” which emerges as a point of tension between the perspectives; and finally; (4) the relevance of the concept of sovereignty itself in addressing these issues.
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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 | 0.020 | 0.026 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.016 | 0.058 |
| Scholarly communication | 0.011 | 0.011 |
| Open science | 0.003 | 0.012 |
| Research integrity | 0.009 | 0.006 |
| Insufficient payload (model declined to judge) | 0.003 | 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 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".