Bibliographic record
Abstract
For about forty years, corporate governance and corporate law focused on minimizing “agency costs” by aligning the interests of shareholders and managers through a series of techniques, including regulatory standards, independent directors, take‐overs and activist shareholders. These means, combined with implicit acceptance of the “Efficient Market Hypothesis” (EMH) reinforced belief that share prices reflected objective corporate performance and that maximizing shareholder wealth was the purpose of the corporation. This author, however, argues that proper corporate governance requires more than just faith in efficient equity markets and strong managerial incentives. Despite the desire for simplicity, there is no one “right” governance model. Governance is highly contextual and, ironically, the existing corporate law and regulation have tended to frustrate dynamic adaption and have led to governance systems that underperform. The author offers Systems Theory as a better way to think about corporate purpose and governance. Systems are more than the sum of their parts, they are comprised of subsystems which in turn are comprised of other subsystems on so on, and the overall health of the system depends on the continued health of each of its essential subsystems, as well as of the larger system. Systems theory counsels against focusing on any single metric (and in favor of the need for new ones — the relevance of metrics inevitably run down over time). Metrics such as profits, employee turnover, and customer satisfaction are not ends in themselves. Rather, they are a source of information about whether the corporation is relevant, resilient and sustainable. The systems challenge is to bring about a paradigm shift that restores connectivity between investors, employees, management, other corporate stakeholders and governments. This will require thinking differently about how the constituent elements interact and produce results.
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 | 0.012 | 0.014 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.010 | 0.057 |
| Scholarly communication | 0.029 | 0.022 |
| Open science | 0.002 | 0.009 |
| Research integrity | 0.006 | 0.015 |
| Insufficient payload (model declined to judge) | 0.003 | 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".