BIG TECH SOVEREIGNTY: PLATFORMS AND DISCOURSE OF SOVEREIGNTY-AS-A-SERVICE
Bibliographic record
Abstract
The notion of digital sovereignty has been mobilized by various stakeholders as a response to platform power. In the last decades, the concept of sovereignty has been applied mainly to State responses to exert power over other sectors and organizations. Therefore, the meanings of digital sovereignty have also been used and disputed by social movements, workers and indigenous communities. But the platform companies also entered into disputes about the meanings of this multifaceted notion. As an update of Californian Ideology, platform companies modulate their discourse to say that they are also concerned with issues of sovereignty. Thus, they are reappropriating the meanings of sovereignty through the launch of programs focused on sovereignty. We named this "Big Tech sovereignty", a provocation to mean how platforms have changed the meanings of sovereignty based on their own interests, such as the renewal of discourses in the context of "Silicon Valley dystopianism". Built on analyzes of sovereignty programs of Amazon, Microsoft and Alphabet/Google, this article argues that "Big Tech sovereignty" is a way of trying to deflate the concept politically, giving it only a commercial and/or personal framework, being more of an expression of the platform power. Through the analysis of these “digital sovereignty” programs, the article demonstrates how companies framed “sovereignty-as-a-service”, especially in terms of digital infrastructures.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.006 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.009 | 0.037 |
| Scholarly communication | 0.017 | 0.019 |
| Open science | 0.001 | 0.008 |
| Research integrity | 0.004 | 0.005 |
| 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".