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Record W3004577089 · doi:10.5539/res.v12n1p47

Socio-Economic Projects Spillovers and Their Influence on Communities Development

2020· article· en· W3004577089 on OpenAlexvenueno aff
Mohamed Buheji

Bibliographic record

VenueReview of European Studies · 2020
Typearticle
Languageen
FieldEconomics, Econometrics and Finance
TopicBusiness, Innovation, and Economy
Canadian institutionsnot available
Fundersnot available
KeywordsSpillover effectEmpowermentPovertyRelevance (law)Value (mathematics)EconomicsBusinessPublic economicsEconomic growthPolitical scienceMicroeconomicsComputer science

Abstract

fetched live from OpenAlex

The purpose of this paper is to gauge the different community socio-economic influences that comes as a result of the impacts and the potential of spillovers brought by these socio-economic projects. Based on the literature review, a theoretical framework is hypothesised. The framework proposes the relation between the socio-economic projects, four stages and their potential impact and spillovers. Selective cases relevant to projects of poverty elimination, healthcare development and women empowerment are carefully studied and dissected. Then, each case is evaluated in relevance to its value chain through a detailed summary. The impact and spillover of each of the sampled projects are illustrated and clarified. Despite its limitations, this study carries important implications as it brings in diverse tools for gauging the different efforts in journey related to creating a socio-economic outcome.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.004
metaresearch head score (Gemma)0.007
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.008
Threshold uncertainty score0.026

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0040.007
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0030.003
Science and technology studies0.0020.004
Scholarly communication0.0050.003
Open science0.0000.008
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0080.000

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 head0.135
GPT teacher head0.259
Teacher spread0.123 · 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 source (direct Gemma or distilled Codex), 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

Citations8
Published2020
Admission routes1
Has abstractyes

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