Bibliographic record
Abstract
This chapter explores how, in the context of international peace and security, the question of human rights is largely overlooked in multilateral discussions of cybersecurity. The creation of the Internet and related digital networks and platforms has necessitated an examination of how those rights apply to individuals in how their use of, and ability to access, those mediums. The inclusion of human rights in two of several reports of UN Groups of Governmental Experts on information and communications technologies (ICTs) have been touched on, but it is useful to have a better understanding of their contents. Balancing the justifiable need to prevent ICTs from being misused to incite or promote violence, with human rights, often represents political, normative, and legislative challenges for states. Canada’s statement reinforced the role that ICTs can play in advancing human rights, and described its efforts to bringing a ‘human rights framing to cybersecurity issues’ as part of its work in the Freedom Online Coalition.
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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 | 0.005 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.008 | 0.032 |
| Scholarly communication | 0.013 | 0.019 |
| Open science | 0.001 | 0.007 |
| Research integrity | 0.004 | 0.007 |
| Insufficient payload (model declined to judge) | 0.014 | 0.001 |
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".