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Record W7124294169 · doi:10.53555/kuey.v30i11.10992

The Balance of Payment Crises in Developing Countries: Causes and Consequences

2024· article· W7124294169 on OpenAlexaboutno aff
Dr. Daleep Kumar, Dr. Richa Ginwal, Dr. Nandan S. Bisht

Bibliographic record

Venuenot available
Typearticle
Language
FieldBusiness, Management and Accounting
TopicInnovation and Socioeconomic Development
Canadian institutionsnot available
Fundersnot available
KeywordsBalance of paymentsDeveloping countryProsperityInefficiencyBalance (ability)Developed countryPer capita incomeResource (disambiguation)

Abstract

fetched live from OpenAlex

This paper delves into the multifaceted challenges faced by developing countries for their economic development and the implications of these challenges on their Balance of Payment (BOP). The primary focus is to explore the potential strategies for overcoming these obstacles. Along with prevailing handicaps in developing countries the research also scrutinizes the per capita real income in developing countries compared to more advanced economies like USA, Canada, Australia and Western Europe. The study also highlights the persistent BOP problems arising from fluctuations in terms of trade, instability of export earnings, unpredictability of foreign capital inflows and inefficiency in domestic policies and institutions. These issues demand a comprehensive and coordinated approach to stabilize BOP and promote long-term economic growth. The paper sheds light on the importance of a balanced approach to policy-making, emphasizing domestic resource utilization while judiciously leveraging both foreign and domestic capital. By adopting comprehensive strategies that enhance productivity, efficiency and competitiveness, developing countries can achieve higher economic growth and bridge the economic gap with advances nations. These efforts will not only contribute to their own prosperity but also promote global economic integration and equality among nations.

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: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.964
Threshold uncertainty score0.988

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 communication0.0010.001
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.025
GPT teacher head0.263
Teacher spread0.238 · 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 designNot applicable
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
Published2024
Admission routes1
Has abstractyes

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