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Record W4220675476 · doi:10.5539/ass.v18n4p19

Capacity Building Initiatives as a Predictor for Sustainability of Projects: A Study of Public Borehole Water Project in Kitui County-Kenya

2022· article· en· W4220675476 on OpenAlexvenueno aff
Onesmus Musau Mwanzia, Raphael Nyonje, Angeline Sabina Mulwa

Bibliographic record

VenueAsian Social Science · 2022
Typearticle
Languageen
FieldBusiness, Management and Accounting
TopicPublic-Private Partnership Projects
Canadian institutionsnot available
Fundersnot available
KeywordsBoreholeNonprobability samplingSustainabilityPopulationSample (material)Null hypothesisBusinessEnvironmental planningGeographyEngineeringMathematicsEnvironmental healthGeotechnical engineeringStatisticsEcology

Abstract

fetched live from OpenAlex

This study focuses on capacity building initiatives influences sustainability of public boreholes water projects. The following was the objective; to assess the extent to which learning from capacity building initiatives, influences the sustainability of public borehole water projects in Kitui County-Kenya. The study tested one null hypothesis to establish whether the association among the study variables was significant. The target population was 366234 project beneficiaries, 396 chairpersons and 8 undertakers. A sample was drawn from this population using a formula adding to 383 project beneficiaries and 167 chairpersons. Purposive sampling was employed to select 8 undertakers in charge of public boreholes in each Sub counties. With R2=0.52, r=0.721, F(2,506) =39.01 at p=0.000, the hypothesis that there is no significant relationship between capacity building initiatives and sustainability of public borehole water projects in Kitui County-Kenya is therefore rejected. The study findings are expected to evidently demonstrate how capacity building initiatives program should be undertaken in public borehole water projects to enhance project sustainability.

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)0.005
metaresearch head score (Gemma)0.002
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 score0.355
Threshold uncertainty score0.655

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0050.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.004
Science and technology studies0.0010.001
Scholarly communication0.0000.003
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 head0.058
GPT teacher head0.319
Teacher spread0.261 · 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

Citations0
Published2022
Admission routes1
Has abstractyes

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