TECHNOLOGICAL SOVEREIGNTY AND SOCIAL JUSTICE: EXPLORING THE INTERSECTION OF FREE SOFTWARE AND INDIGENOUS KNOWLEDGE
Bibliographic record
Abstract
Questions of independence and sovereignty have long been present with regards to the Internet. In 1996, for instance, John Perry Barlow published his now well-known “Declaration of the Independence of Cyberspace”. Twenty-five years later, notions like “digital sovereignty”, “data sovereignty” and “technological sovereignty” are increasingly used in public debates. This presentation will explore “technological sovereignty” but through the lens of Indigenous perspectives as well as those of social movements inspired by free software activism. These two perspectives seem to share what can be called an anti-hegemonic perspective on technological sovereignty. While they may reinforce each other, they also differ on many perspectives. It is noted for instance that the philosophy of information sharing in free and open-source software might foster the usage and misappropriation of knowledge held by Indigenous communities (Christen, 2012; Gida, 2019). This analysis will prolong a previous study by the authors which identified different discursive trends around sovereignty (anonymous reference). Methodologically, our approach is grounded in discourse analysis and reviews of academic and activist literature that has mobilized metaphors of digital sovereignty. What is the role of the metaphor of “sovereignty” in reconfiguring Indigenous and social justice activism, in relation to the Internet? What are the commonalities between these perspectives? How are they reinforcing or contradicting each other? We intend to contribute to the theme of this year’s AOIR conference – Independence – by looking at the critical discourses of Indigenous people and social activists through the lens of the metaphor of digital (technological/data) sovereignty.
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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 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.005 |
| Science and technology studies | 0.019 | 0.085 |
| Scholarly communication | 0.021 | 0.024 |
| Open science | 0.002 | 0.025 |
| Research integrity | 0.005 | 0.006 |
| Insufficient payload (model declined to judge) | 0.005 | 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".