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Record W4221036053 · doi:10.18280/ijsdp.170106

Implementations and Challenges of Sustainable Development Goals in Developing Nations: In the Case of South Gondar Zone, Ethiopia

2022· article· en· W4221036053 on OpenAlexvenueno aff
Matebe Tafere Gedifew, Destaw Amare Lakew

Bibliographic record

VenueInternational Journal of Sustainable Development and Planning · 2022
Typearticle
Languageen
FieldComputer Science
TopicEconomic Growth and Development
Canadian institutionsnot available
Fundersnot available
KeywordsImplementationSample (material)Thematic analysisSustainable developmentPillarQualitative propertyProcess managementRegression analysisMultilevel modelQualitative researchComputer sciencePolitical scienceBusinessEngineeringSociologySocial science

Abstract

fetched live from OpenAlex

The purpose of this paper is to examine the implementations and challenges of SDGs in South Gondar. To attain this purpose, survey questionnaires were administered on a sample of 176 employees. While to the qualitative analysis interview and document observations were used. The study employed mixed methods research approach of parallel concurrent research design. For quantitative data analysis, one sample t-test, Pearson correlation and hierarchical linear regression were used. To the qualitative data, thematic analysis was employed. The study found that the SDG implementation in the study region was "moderate", with average differences between institutions. The major challenges facing the implementation of SDGs are unrealistic goal setting, lack of political commitment, lack of participation, lack of clear policy guide, lack of synergy, lack of capability and over emphasis on one pillar of development. This indicated that both key identified institutional challenges and goal setting characteristics determine the implementation of the SDGs in the study area. Based on this, the study recommends that the study area should set policy goals that are implementable. There should be also participation of the target beneficiaries in the SDGs implementation.

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)0.003
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 score0.522
Threshold uncertainty score0.509

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0010.001
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 head0.032
GPT teacher head0.282
Teacher spread0.250 · 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
Published2022
Admission routes1
Has abstractyes

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