The Chilling Effects of Surveillance and Human Rights: Insights from Qualitative Research in Uganda and Zimbabwe
Bibliographic record
Abstract
Abstract States are increasingly developing and deploying large scale surveillance and AI-enabled analytical capabilities. What is uncertain, however, is the impact this surveillance will have. Will it result in a chilling effect whereby individuals modify their behaviour due to the fear of the consequences that may follow? Understanding any such effect is essential: if surveillance activities interfere with the processes by which individuals develop their identity, or undermine democratic processes, the consequences may be almost imperceptible in the short term but profound over the long term. Currently, surveillance-related chilling effects are not well understood, meaning that insufficient weight is given to their potentially society-wide impacts. This article seeks to help redress this balance. Drawing on empirical research in Zimbabwe and Uganda it highlights how State surveillance has chilled behaviour, with significant implications for rights essential to individual development and democratic functioning, specifically the rights to freedom of expression and to freedom of assembly. Importantly, this qualitative research identifies a pattern of common themes or consequences associated with surveillance in general, allowing us to move beyond hypothetical or individual experiences, and providing a greater understanding of the nuances of surveillance-related effects that can help inform decision-making surrounding large scale digital surveillance.
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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 | 0.037 | 0.060 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.013 | 0.025 |
| Scholarly communication | 0.007 | 0.009 |
| Open science | 0.002 | 0.011 |
| Research integrity | 0.002 | 0.003 |
| 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".