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Record W4200331529 · doi:10.33423/jabe.v23i5.4570

An Analysis of Economic Philosophy and Leadership in Ancient India

2021· article· en· W4200331529 on OpenAlexvenueno aff
Sushma Shukla

Bibliographic record

VenueJournal of Applied Business and Economics · 2021
Typearticle
Languageen
FieldEconomics, Econometrics and Finance
TopicGlobal Financial Crisis and Policies
Canadian institutionsnot available
Fundersnot available
KeywordsPoliticsEmpireJurisprudenceEconomic ThoughtEuropean unionAdam smithHistory of economic thoughtPhilosophy and economicsWork (physics)Economic analysisPolitical philosophyEconomic historyPolitical economyPolitical scienceSocial scienceEconomicsSociologyLawNeoclassical economicsClassical economics

Abstract

fetched live from OpenAlex

Economics was a part of social and political thoughts in general for most of history. In the eighteenth century, Adam Smith saw economics as a subset of jurisprudence. However, the seeds of economic analysis were planted long before; in ancient India, societies exercised economic theories and principles during the 3rd century BCE. This paper investigates the economic leadership in ancient India between the 3rd century BCE and the 3rd century C.E. During that era, a philosopher, economist, jurist, and royal advisor named Chanakya authored the ancient Indian political treatise the Arthshastra. He has discussed many economic theories like economic growth, tax, and mixed economy in his book. He is considered the pioneer of political economics in India, and his work is believed to as an essential precursor to classical economics. This paper evaluates the theories of Chanakya by a comparative analysis of political and economic union between the modern-day the European Union and the Mauryan Empire of ancient times.

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)0.001
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: Theoretical or conceptual
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.013
Threshold uncertainty score0.064

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0020.002
Science and technology studies0.0030.008
Scholarly communication0.0030.001
Open science0.0000.002
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0030.000

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 head0.062
GPT teacher head0.224
Teacher spread0.162 · 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 designTheoretical or conceptual
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

Citations3
Published2021
Admission routes1
Has abstractyes

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