Impact of the Digital Divide on Computer Use and Internet Access on the Poor in Nigeria
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
<p>We recruited 20 community members in Ido Local Government Area, Oyo state and Yewa Local Government Area, Ogun state in Nigeria to explore experiences and perceptions of Internet access and computer use. Face-to-face interviews were conducted using open-ended questions to collect qualitative data regarding accessibility of information and communication technology. Twenty low-income community members volunteered to participate in the study. The results centered around affordability of computers and Internet access, exposure to information on the Internet, increasing access to the Internet, training on computer use, benefits for job searching, and networking. The results indicated the lack of Internet access, affordability of computers and Internet usage, poverty, lack of computer skills, and poor infrastructures were contributors to the digital divide.</p>
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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