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Record W2289251868 · doi:10.5539/enrr.v6n1p67

Institutional Arrangements and Management of Environmental Resources in Ethiopia

2016· article· en· W2289251868 on OpenAlexvenueno aff
Sisay Nune Hailemariam, Teshome Soromessa, Demel Teketay

Bibliographic record

VenueEnvironment and Natural Resources Research · 2016
Typearticle
Languageen
FieldEnvironmental Science
TopicRangeland Management and Livestock Ecology
Canadian institutionsnot available
Fundersnot available
KeywordsIncentiveBusinessEnvironmental planningNatural resourceAgricultureEnvironmental resource managementLegislationNatural resource managementPolitical scienceGeographyEconomics

Abstract

fetched live from OpenAlex

<p class="1Body">The study was conducted in three main eco-regions, namely Bale Mountains, South-West and Semien Eco-Regions in Ethiopia with the following objectives - to: (i) review the current institutional arrangements in terms of rights and responsibilities, planning system, capacity, and motivation of local communities for the management of environmental resources in Ethiopia in general, and the forest sector in particular and (ii) assess constraints for the successful implementation of policies/legislation, strategies, programs, projects and actions at a landscape level. Focus group discussion and semi-structured questionnaires were used to collect data. Purposive sampling method was employed to select respondents. Environmental resources conservation and management (ERCM) institutions considered in this study were agriculture and natural resources, water, irrigation and electricity, land and environmental protection, local administration, and road authorities. Within the above-mentioned institutions a total of 56 questionnaires were administered and 48 interviews were conducted. The results show that the capacity of the existing institutions is constrained by lack of clear rights and responsibilities, absence of common result framework, absence of common planning system, high staff turnover, absence of spatial planners and failure to respond to the demands of community-based organizations. The institutional arrangements need critical review and analyses in order to design responsible institutions for ERCM at all levels, which includes availing knowledgeable and all-rounded professionals at all levels, with proper incentive mechanisms, who would be able to cope with future challenges emanating from climate change and other social tensions.</p>

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 categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.179
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

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

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 head0.017
GPT teacher head0.262
Teacher spread0.246 · 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 designObservational
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

Citations11
Published2016
Admission routes1
Has abstractyes

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