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Record W4319832940 · doi:10.1002/jid.3737

Funding schemes for infrastructure investment and poverty alleviation in Africa: Evidence from Guinea‐Bissau

2023· article· en· W4319832940 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.
fundA Canadian funder is recorded on the work.

Bibliographic record

VenueJournal of International Development · 2023
Typearticle
Languageen
FieldEconomics, Econometrics and Finance
TopicFiscal Policy and Economic Growth
Canadian institutionsUniversité Laval
FundersUniversidade Federal do ParanáCoordenação de Aperfeiçoamento de Pessoal de Nível SuperiorUniversité de Sherbrooke
KeywordsSocial accounting matrixComputable general equilibriumInvestment (military)EconomicsPovertyExternalityPoverty reductionDebtPer capitaInequalityDevelopment economicsEconomic growthPublic economicsBusinessFinanceMacroeconomicsPopulationPolitical science

Abstract

fetched live from OpenAlex

Abstract This study examines the economic impacts of an infrastructure investment programme in Guinea‐Bissau for the period 2014–2030 using a dynamic computable general equilibrium model. Social accounting matrix (SAM) takes into account informal activities, and the model integrates funding schemes for infrastructure investment. We found that debt‐funded infrastructure investment will generate positive macro‐ and micro‐level externalities in terms of growth and well‐being outcomes across household groups in the urban and rural environments and contribute to inequality reduction. However, direct tax funding scheme is not the best economic development alternative for a country with a low per capita income.

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.

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 score0.124
Threshold uncertainty score0.350

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 head0.082
GPT teacher head0.264
Teacher spread0.182 · 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