Digital Inclusion and the Digital Divide in Rural Seychelles Communities, 2003
Bibliographic record
Abstract
This study examines digital inclusion and the digital divide in rural communities of Seychelles, a small archipelagic nation in the Indian Ocean. A cross-sectional survey was conducted with a sample of 500 rural residents across all inhabited islands of Seychelles. Data were collected through structured questionnaires designed to measure access to technology, frequency of use, perceived benefits, and barriers to digital participation. The findings indicate that while internet connectivity is widely available in urban areas, it remains sparse in remote villages, creating a significant digital divide. Device ownership shows higher proportions among younger respondents (aged 15-30) compared to older generations, suggesting generational gaps in technology adoption. Rural Seychelles communities face substantial challenges in bridging the digital divide, with limited access and low levels of digital literacy being major barriers. The study highlights the need for targeted interventions aimed at enhancing connectivity and promoting digital skills training programmes. Policy recommendations include prioritising infrastructure investments to improve internet penetration in rural areas and implementing educational initiatives to address the digital divide among less digitally literate populations.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.000 | 0.000 |
| 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".