Increasing Public Participation in Local Government by Means of Mobile Phones: What do South African Youth Think?
Bibliographic record
Abstract
Apathy towards political participation is of concern for many countries throughout the world, and for many people political participation means no more than voting in an election. The South African Constitution makes several provisions for public participation but E-government solutions are not suited to the South African context, where fixed-line internet penetration is dramatically lower than that of mobile phones. Mobile phones cut across socio-economic barriers and have changed the way we communicate. They have been used to mobilise people in different parts of the world, more notably those who were passive politically, into action. This research set out to investigate whether using mobile phones to increase participation in local government would be acceptable or not. A mixed-method research was conducted in Cape Town, South Africa, amongst youths between the ages of 18 and 35 who had no access to fixed-line internet from either home or work. Constructs from a modified UTAUT model and Social Capital Theory were used to determine the individual intention to use government mobile service if they were made available. It was found that there is not only great interest in using mobile phones to interact with government mobile services, but also to interact with other members of the community. The ability to report on corruption and service delivery problems was particularly welcome.
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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 | 0.003 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".