A Value Assessment of Personal Data: Towards Greater Privacy Consciousness in Africa
Bibliographic record
Abstract
The world has become a global village, as the digital age has increased our interconnectedness. A crucial component in this digitalization era is personal information-based data or big data; literally, the string that connects many modern devices and web applications most people today cannot live without. Accordingly, a reinforced consciousness drive towards personal data protection is pivotal. This is the core of this article, and our focus is Africa. On the one hand, it can be argued that African legal regimes contribute to a situation where laws are either unnecessarily delayed or, if they exist, do not necessarily address the peculiar circumstances of the clime, but rather use a 'cut and paste' approach. On the other hand, there is the question of how much responsibility individuals impose on themselves, in terms of safeguarding their personal information when exploring the digital age, we live in. This article takes a comparative approach to consider both factors, emphasizing the critical need for improved privacy consciousness in African countries, as the number of its people using smart devices, the internet, and other data-based applications, grows. The work is particularly relevant, considering that primary data protection laws are evolving in the region.
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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 | 0.008 | 0.014 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.006 | 0.014 |
| Scholarly communication | 0.008 | 0.011 |
| Open science | 0.001 | 0.007 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.002 | 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".