The Vested Way: A Model of Formal Relational Contracts
Bibliographic record
Abstract
In 1963, Stewart Macaulay exposed a paradox at the heart of modern contract law when he published the first academic account of what is now known as "relational contracts": the theory and practice of contracting diverge sharply under some circumstances but not others for reasons that neither contract theorists nor contract practitioners were able to explain. 1 For more than half a century, economists, legal academics, managers, and lawyers have attempted to resolve that paradox.While many important insights have emerged from that debate, no attempt to resolve the paradox has been able to achieve universal acceptance among economists, legal academics, managers, and lawyers.Yet even in the absence of a universally accepted explanation for when they should and should not be used, recent court cases in the U.K. and Canadian high courts demonstrate that relational contracts remain important governance tools for many * Kate Vitasek is an international authority recognized for her award-winning research and the Vested business model for highly collaborative relationships.She is the author of six books on Vested and a faculty member at the University of Tennessee.She has been lauded by World Trade magazine as one of the "Fabulous 50+1" most influential people impacting global commerce and has shared her insights on CNN International, Fox Business News, Bloomberg, and NPR.1. Stewart Macaula,
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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 | 0.004 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.003 | 0.009 |
| Scholarly communication | 0.008 | 0.015 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.004 | 0.004 |
| Insufficient payload (model declined to judge) | 0.018 | 0.002 |
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".