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Record W3034893750 · doi:10.1163/17087384-12340056

The Right to Privacy v National Security in Africa: Towards a Legislative Framework Which Guarantees Proportionality in Communications Surveillance

2020· article· en· W3034893750 on OpenAlexvenueno aff
Justice Alfred Mavedzenge

Bibliographic record

VenueAfrican Journal of Legal Studies · 2020
Typearticle
Languageen
FieldSocial Sciences
TopicHuman Rights and Development
Canadian institutionsnot available
Fundersnot available
KeywordsProportionality (law)LegislatureThe Right to PrivacyOrder (exchange)National securityAuthorizationComputer securityInformation privacyDeclarationBusinessInternet privacyLaw and economicsPolitical scienceLawHuman rightsComputer scienceEconomics

Abstract

fetched live from OpenAlex

Abstract Governments often resort to communications surveillance in order to combat threats against national security. Communication surveillance infringes upon the right to privacy. In order to protect privacy, international law requires communication surveillance to be proportionate. However, very little has been written to justify why this right deserves such protection in Africa, given counter-arguments suggesting that where national security is threatened, the state must be permitted to do everything possible to avert the threat, and the protection of privacy is an inconvenience. This article addresses these counter-arguments by demonstrating that the right to privacy deserves protection because it is as important as defending national security. It analyses approaches taken by selected African countries to regulate authorisation of communication surveillance. This article questions the assumption that prior judicial authorisation is the ideal approach to regulating communication surveillance in order to guarantee proportionality, and it suggests a need to consider other alternatives.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.038
metaresearch head score (Gemma)0.047
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: Theoretical or conceptual
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.038
Threshold uncertainty score0.198

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0380.047
Meta-epidemiology (narrow)0.0000.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0020.001
Science and technology studies0.0060.021
Scholarly communication0.0120.009
Open science0.0010.006
Research integrity0.0080.011
Insufficient payload (model declined to judge)0.0030.000

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.

Opus teacher head0.092
GPT teacher head0.376
Teacher spread0.284 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designTheoretical or conceptual
Domainnot available
GenreEmpirical

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".

Quick stats

Citations3
Published2020
Admission routes1
Has abstractyes

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