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Record W2799696551 · doi:10.5539/enrr.v8n2p69

A Neglected Resource in Community Development: Participation of Ethiopian Academics in the Development of their Communities

2018· article· en· W2799696551 on OpenAlexvenueno aff
Worku Legesse, Getnet Tadele, Aynalem Adugna, Helmut Kloos

Bibliographic record

VenueEnvironment and Natural Resources Research · 2018
Typearticle
Languageen
FieldSocial Sciences
TopicReligion, Society, and Development
Canadian institutionsnot available
Fundersnot available
KeywordsLivelihoodCommunity developmentEconomic growthResource (disambiguation)Bridge (graph theory)Political scienceSociologyPublic relationsGeographyMedicineAgriculture

Abstract

fetched live from OpenAlex

This article examines three community development projects initiated by university educated individuals in or near their places of birth with the aim of presenting evidence that academics can be instrumental in contributing to development in their communities. The three projects used different approaches and resources. The project in Arya Jawi Kebele was developed and managed by a church-linked family association that aimed at broadly based development of this rural district guided by the theological principles of an Ethiopian evangelical church. The project in Kersole Village was initiated and managed by six university educated brothers, four of whom are currently living in the United States. Their primary objective was to help their family and secondly to improve livelihoods in the community. The project in the small town of Azena, conceived and fostered by a professor at Addis Ababa University, focused on the construction of a bridge over a river and several schools, with financial support mainly from several international NGOs. The academics’ familiarity with the needs of and their acceptance by the communities facilitated interaction with local leaders, organizations and craftsmen during the planning and implementation processes and promoted community participation.

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)0.007
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.553
Threshold uncertainty score0.877

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0070.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0010.002
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.001
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 head0.107
GPT teacher head0.374
Teacher spread0.267 · 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.

The models applied no category: nothing in the taxonomy fit this work.
Study designQualitative
Domainnot available
GenreEmpirical

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

Citations1
Published2018
Admission routes1
Has abstractyes

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