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Record W2797294410 · doi:10.5539/jas.v10n5p52

Challenges to National Park Conservation and Management in Ethiopia

2018· article· en· W2797294410 on OpenAlexvenueno aff
Firew Bekele Abebe, Solomon Estifanos Bekele

Bibliographic record

VenueJournal of Agricultural Science · 2018
Typearticle
Languageen
FieldEnvironmental Science
TopicConservation, Biodiversity, and Resource Management
Canadian institutionsnot available
FundersStockholms UniversitetHaramaya University
KeywordsNational parkEnvironmental planningNatural resourceEnvironmental resource managementBusinessPopulationConsumption (sociology)GeographyNatural resource economicsPolitical scienceEconomics

Abstract

fetched live from OpenAlex

National parks are areas of land protected to conserve native plants and animals and their habitats, places of natural attractiveness, historic heritage and native cultures. The objective of this review paper was to identify challenges affecting conservation and management of national parks in Ethiopia and based on review results, to suggest management strategies that can bring solutions to the problems. Lack of sense of ownership, limited awareness, population growth, lack of coordination, conflicts over resources, Issues of boundary/Lack of Boundary, invasive species, illegal charcoal production, climate change, and poverty are the identified challenges that are affecting the conservation and management of national parks in Ethiopia. Developing sense of ownership within community, awareness creation and development, collaborative approach and consultation among stakeholders, co-management and resolution of border issues, reduction of free grazing, invasive species utilization, care during introduction and biological control, reduction of fuel-wood consumption and increase carbon sequestration, improving incomes, institutional and policy reforms are the suggested strategies that can bring solutions to the problems.

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 categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.090
Threshold uncertainty score0.150

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
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 head0.028
GPT teacher head0.242
Teacher spread0.213 · 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 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

Citations22
Published2018
Admission routes1
Has abstractyes

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