Assessing the implications of digital contact tracing for COVID-19 for human rights and the rule of law in South Africa
Bibliographic record
Abstract
The article argues that the establishment of centralised and aggregated databases and applications enabling mass digital surveillance, despite their public health merits in the containment of the COVID-19 pandemic, is likely to lead to the erosion of South Africa's constitutional human rights, including rights to equality, privacy, human dignity, as well as freedom of speech, association and movement, and security of the person. While derogation clauses have been invoked, thereby limiting International Covenant on Civil and Political Rights clauses and enabling the mass collection of location data only for contact tracing purposes under the Disaster Management Act, a sustained breach of these rights may pose an impending threat to the human rights framework in South Africa. Any proposed digital contact tracing technologies in their design, development and adoption must pass the firm legal muster and adhere to human rights prescripts relating to user-centric transparency and confidentiality, personal information, data privacy and protection that have recently been enacted through the latest development on Protection of Personal Information Act.
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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 | 0.020 | 0.069 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.014 | 0.027 |
| Scholarly communication | 0.019 | 0.015 |
| Open science | 0.002 | 0.013 |
| Research integrity | 0.010 | 0.011 |
| Insufficient payload (model declined to judge) | 0.010 | 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".