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Record W1973945101 · doi:10.5539/ijef.v3n4p162

Projecting the Possible Impacts of the National Economic Empowerment and Development Strategy (NEEDS) on Human Development in Nigeria

2011· article· en· W1973945101 on OpenAlexvenueno aff
O. J. K. Ogundele, Abubakar Hassan

Bibliographic record

VenueInternational Journal of Economics and Finance · 2011
Typearticle
Languageen
FieldSocial Sciences
TopicAfrican Education and Politics
Canadian institutionsnot available
Fundersnot available
KeywordsEmpowermentFundamental human needsHuman development (humanity)PovertyEconomic growthMaslow's hierarchy of needsSustainable developmentBusinessNational developmentHuman resourcesEconomicsPolitical scienceManagementPsychology

Abstract

fetched live from OpenAlex

The study investigated the possible impact of National Economic Empowerment and Development Strategy (NEEDS) on human development in Nigeria. NEEDS is a home-groomed economic and social package focused on fighting poverty and enthroning a people-centered developmental programmes. The paper evaluates each of the major thrusts of NEEDS by highlighting their respective demands for new patterns of human development. Also, collaborative and interactive model of human development is presented. The paper highlights three critical core areas that can enthrone the success of NEEDS for sustainable development. These are evangelistic development of indigenous professionals by providing needed supports in terms of training and development, infrastructural facilities and finance, enforcing the practice of business and social ethics after effective mobilization of the people psychic with the values reorientation component of NEEDS, and applying strict and swift sanctions on violations of social and economic ethics to serve as deterrent for others. It is expected therefore that through effective and efficient implementation, NEEDS will serve as a useful strategy and tool for economic development of Human resources.

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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.726
Threshold uncertainty score0.175

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 studies0.0000.000
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 head0.075
GPT teacher head0.340
Teacher spread0.265 · 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

Citations4
Published2011
Admission routes1
Has abstractyes

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