Bridging the Privacy Gap: Analyzing Nigerian Disclosure Behaviors and Developing Culturally Relevant Interventions
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
The proliferation of the internet has significantly increased social media usage in sub-Saharan Africa, particularly in Nigeria, where over 163 million users engage in diverse activities. However, existing privacy and security studies and solutions are primarily tailored for Western, Educated, Industrialized, Rich, and Democratic (WEIRD) countries, leaving Nigerian users underserved. This paper examines Nigerian privacy and disclosure behavior across various domains using the privacy calculus framework to develop effective, relatable, and applicable interventions. Three studies are described: one on financial applications, another on social media birthday disclosures, and a third on intimate contexts such as sexting. The findings will inform the development of gamified, culturally relevant privacy education tools aimed at enhancing privacy awareness and behavior among Nigerians.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.003 | 0.000 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it