Bibliographic record
Abstract
Natural laws that allows living things to become self-regulating and for social biota to become self-governing provide an evolutionary tested model for re-inventing corporate governance. The science of control and communication in the animal and the machine described as “cybernetics” can be extended to social organizations by using bytes as the unit of analysis to create a natural science of governance. The science explains why top down command and control hierarchical organizations cannot efficiently or reliably manage complexity. Reliable survival also requires bottom up feedback communication and control. This explains why small-brained low intelligence creatures can sustain their existence in complex, uncertain dynamic environments when organizations governed by large brained highly intelligent humans cannot. Organizations like VISA international, The John Lewis Partnership and the Mondragon cooperatives provide evidence of the ability of organization with networks of both top down and bottom up system of governance can survive and prosper over business cycles and generations of management. A process is identified by which firms could move towards self-regulating network governance. This would allow the role of regulators to change to monitoring the integrity of self-governing processes. In this way re-inventing governance would reduce the role, size and cost of government.
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 | 0.013 | 0.023 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.017 |
| Scholarly communication | 0.007 | 0.009 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.005 | 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".