Knowledge Sharing between Local Government and Rural Remote Communities in Tanzania
Bibliographic record
Abstract
Social, economic, and cultural factors are known to influence the knowledge sharing process between governments and rural communities. There is evidence that the success of ICT for development partnerships depends on a broadly identified “local context” and involvement of local communities. This chapter describes a survey of citizens about their information needs and modes of reception as well as a pilot study of Village Information Officers. Utilizing new technologies such as mobile phone communication and community radio broadcasting in local languages is identified by remote and rural study and survey participants as a valuable alternative to traditional government methods for communicating with citizens. Rural people identified gaps in knowledge related to health, education, and economic activities. These three broad categories of knowledge are important for effective poverty reduction efforts of government. Due to the poor reach of newspapers or other forms of print and broadcast media, face-to-face communication and cell phones were mentioned by more than 60% of the respondents in Kilosa district as techniques used in accessing government information. The positive results achieved by Village Information Officers in responding to gaps in knowledge regarding government services and support for development efforts has led to further demand for replication of the pilot study to support pastoralists, emergency preparedness, and wildlife conservation.
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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 | 0.001 | 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 | 0.004 | 0.002 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".