Reclaiming digital sovereignty: A roadmap to build a digital stack for people and the planet
Bibliographic record
Abstract
This policy paper outlines a progressive reform agenda to enhance digital sovereignty for people and the planet with the following 4 key proposals: 1. Offer a democratic, public-led digital stack that shall include: 1) Digital infrastructure as a service (for training, processing and developing digital solutions) provided by non-profit and democratic international consortia; 2) universal platforms, such as search engines and foundation AI models, that should be a commons governed by new public institutions with state and civil society representation; and 3) a public marketplace where companies can offer their computing services without lock-ins. To assure demand, states shall procure from this marketplace and end contracts with Big Tech. 2. Craft a research agenda focused on digital developments that could solve collective problems and enhance human capacities and that consider the ethical, economic, ecological, and political impacts of technology, including of AI applications. For this end, public knowledge networks led by a new public international research agency (or agencies) could counterbalance the concentration of private and closed science. 3. Ground digital sovereignty in an ecological internationalism an antidote to individual government surveillance and power abuses that also minimises the resources needed to build a democratic, public digital stack. 4. Establish strict mechanisms to dismantle state surveillance or misappropriation of collective solutions by specific governments. Multilateral agreements on principles and rules for the internet are indispensable safeguards for building autonomous and democratically governed institutions and solutions. To complement and facilitate all the above, the authors further lay out a strategy on retrofit markets’ authorities for the digital age and implement measures to properly regulate and tax revenues and data and knowledge capture of dominant technology companies. The new policy framework outlined in the paper also aims at protecting labour and enhancing its creative autonomy while contributing to the reinforcement of human and civil rights. One aspect could be a safety net in which states offer training and employment for the development and operation of the public-led digital stack.
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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 | 0.019 | 0.020 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.008 | 0.022 |
| Scholarly communication | 0.022 | 0.055 |
| Open science | 0.003 | 0.025 |
| Research integrity | 0.018 | 0.016 |
| Insufficient payload (model declined to judge) | 0.026 | 0.007 |
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".