Creating Virtuous Humanistic Managers and Organisations with Polycentric Corporate Charters
Bibliographic record
Abstract
This paper explains how the virtue of managers and organizations can be promoted with corporate constitutions that distribute power with polycentric governance as described by Ostrom. It allows paradoxical humanistic behavior described as “tensegrity” to emerge. DNA generates behavioral variety, like approach ~ avoidance, suspicious ~ trusting, cooperative ~ competitiveness, and so on to drive adaption and survival. This can likewise become embedded in organizations with corporate charters that introduce bottom-up stakeholder polycentric governance. The introduction of dynamic humanistic behavior means that the static dominant models reviewed by Meckling & Jensen lose their relevancy. We identify how a self-funding tax incentive for shareholders could introduce both polycentric governances with systemic re-birthing of enterprises over the life of a patent. This neglected process to distribute corporate ownership and control to bioregional citizens creates a process to form circular self-governing economies. A universal well-being dividend income becomes established to sustain all citizens with fewer taxes and government. This provides a way for simplifying on a bottom-up basis the complexity of managing both operating and existential risks to humanity locally and globally. UN SDGs are promoted. Democracy is enriched to self-determine population levels for the eternal well-being of people and the planet.
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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 | 0.009 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.005 | 0.022 |
| Scholarly communication | 0.009 | 0.005 |
| Open science | 0.001 | 0.009 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".