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Record W1978848650 · doi:10.5539/ass.v4n1p30

Kautilya’s Arthashastra and Perspectives on Organizational Management

2007· article· en· W1978848650 on OpenAlexvenueno aff
Balakrishnan Muniapan

Bibliographic record

VenueAsian Social Science · 2007
Typearticle
Languageen
FieldSocial Sciences
TopicWorkplace Spirituality and Leadership
Canadian institutionsnot available
Fundersnot available
KeywordsInterpretation (philosophy)PrideContext (archaeology)SociologyHuman resource managementManagementKnowledge managementPublic relationsPolitical scienceHistoryComputer scienceEconomicsLaw

Abstract

fetched live from OpenAlex

This paper explores the Arthashastra of Kaultilya, an ancient Indian literature (4th Century B.C.); and it’s perspectives on organizational management today. Chinmayananda (2003) asserted that from time to time there is a need to look and re-look at the ancient literatures and provide intelligent interpretation and re-interpretation to apply effectively in the context of modern management. The methodology used for this purpose is called hermeneutics; which is a study, understanding and interpretation of ancient text. It is one of the qualitative research methodology used in social science. The foundations of management in organization are revealed from the Arthashastra, which can provide guidance to present managers and leaders of organizations. In his Arthashastra, Kautilya takes an inside-out approach to management, which is self management first before management of every other thing. He advised the future organizational managers and leaders to firstly conquer the enemies within such as desires, anger, greed, arrogance, infatuation, envy, pride or ego and foolhardiness, as it is often said that one who conquers the self, conquers all. The prospects of analysis of Kautilya’s Arthashastra in other areas of organizational management such as strategic management, human resource management and financial management can be considered for future research.

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)0.002
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesScience and technology studies
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.970
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0020.003
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 head0.038
GPT teacher head0.321
Teacher spread0.284 · 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.

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

Citations19
Published2007
Admission routes1
Has abstractyes

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