Kautilya’s Arthashastra and Perspectives on Organizational Management
Bibliographic record
Abstract
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 machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.006 | 0.018 |
| Scholarly communication | 0.006 | 0.005 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".