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Knowledge Sharing between Local Government and Rural Remote Communities in Tanzania

2014· book-chapter· en· W2503981427 on OpenAlexaff
Chantal Philips, Wulystan Pius Mtega, Arja Vainio-Mattila

Bibliographic record

VenueAdvances in finance, accounting, and economics book series · 2014
Typebook-chapter
Languageen
FieldSocial Sciences
TopicKnowledge Management and Sharing
Canadian institutionsUniversity of Guelph
Fundersnot available
KeywordsGovernment (linguistics)Public relationsPhoneBusinessContext (archaeology)Knowledge sharingLocal governmentEconomic growthPolitical scienceGeographyKnowledge managementPublic administration

Abstract

fetched live from OpenAlex

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.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Other · Consensus signal: Other
Teacher disagreement score0.931
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
Scholarly communication0.0000.002
Open science0.0000.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.015
GPT teacher head0.242
Teacher spread0.228 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

Study designNot applicable
Domainnot available
GenreOther

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".

Quick stats

Citations0
Published2014
Admission routes1
Has abstractyes

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