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Record W2895741790 · doi:10.3126/rnjds.v1i1.21272

Fiscal decentralization in Buddhist economics: An interactive analysis from Lumbini

2018· article· en· W2895741790 on OpenAlexaff
Indra Prasad Bhusal

Bibliographic record

VenueResearch Nepal Journal of Development Studies · 2018
Typearticle
Languageen
FieldEconomics, Econometrics and Finance
TopicFiscal Policy and Economic Growth
Canadian institutionsHorizon College and Seminary
Fundersnot available
KeywordsDecentralizationGautama BuddhaIdeologyProduct (mathematics)EconomicsBuddhismPositive economicsPublic economicsEconomic systemPolitical scienceLawPoliticsMarket economy

Abstract

fetched live from OpenAlex

The main objective of the article is to identify the basic elements of Buddhists Economics applicable to fiscal decentralization. The study reviews the Buddhists Economics and Fiscal Decentralization from global perspective. Fiscal decentralization is the process to deliver the power and resources to the grass root level of governance. If the governing body is able to impose the Buddha’s ideology in the policy, certainly resource allocation conflict will be minimized.Methodologically the researcher has conducted interaction programs among Buddhists economists, scholars and academicians available in Lumbini– the birth place of Buddha. The author has analyzed the facts matching with literature review and focused group discussion in Lumbini. The study has identified elements of fiscal decentralization from Buddhist Economics. These elements can be a guideline for noble scholars, academicians, researchers and policy makers of fiscal decentralization from Buddhists economics.Research Nepal Journal of Development Studies Vol.1(1) 2018 32-41

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)0.001
metaresearch head score (Gemma)0.002
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.018
Threshold uncertainty score0.036

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.002
Science and technology studies0.0020.002
Scholarly communication0.0030.002
Open science0.0000.002
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0060.000

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 head0.166
GPT teacher head0.380
Teacher spread0.214 · 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 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
Published2018
Admission routes1
Has abstractyes

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