Addressing Mass State Surveillance Through Transparency and Network Sovereignty, within a Framework of International Human Rights Law - a Canadian Perspective
Bibliographic record
Abstract
Mass state surveillance of communications traffic on a global scale poses a significant challenge to internet governance efforts that seek to maintain consistency with well established human rights law. This paper identifies key features of the internet surveillance conducted by the Five Eyes security alliance and the threats these pose to privacy and other rights founded in multi-lateral legal agreements. Responding to these threats, a world-wide coalition of pro-privacy civil society organizations have developed a set of 13 Necessary and Proportionate Principles for evaluating surveillance legislation and practices. I highlight Principle #9 – Transparency, and show how through the development of an internet mapping platform, IXmaps.ca, it can be applied to making more visible the geographic specificities of internet routing, particularly where data travels and may be exposed to interception by surveillance facilities installed at major internet exchanges. As an internet policy transparency tool, IXmaps helps make visible the widespread phenomenon of ‘boomerang’ routing, in which communication that both originates and terminates in one country transits another. In the Canadian context, this means data traveling via the U.S. being exposed to NSA surveillance, while losing Canadian legal and constitutional protections. Furthermore, this tool reveals that most internet communications between Canada and a third country is routed through the U.S., facing similar interception risks. Both routing patterns pose a threat to privacy as well as ‘network sovereignty,’ treated as the ability of a nation to maintain effective control over vital aspects of its internet infrastructure and operations. When consistent with both international human rights law and the integrity of the internet as a unified open global communication medium, transparency and network sovereignty provide important principles of internet governance.
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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 | 0.011 | 0.020 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.016 | 0.030 |
| Scholarly communication | 0.022 | 0.009 |
| Open science | 0.003 | 0.007 |
| Research integrity | 0.005 | 0.008 |
| Insufficient payload (model declined to judge) | 0.006 | 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".