A pricing‐error rule on share distribution in equity joint ventures: The Bayesian approach
Bibliographic record
Abstract
Equity joint ventures (EJVs) are a popular governance mode of inter‐firm cooperation that has attracted substantial research attention. The literature, however, still lacks a precise rule for the parents to follow in splitting the equity shares of an EJV, although share distribution is critical to almost all aspects of the co‐ownership relationship. In this study, we fill this literature gap by taking the Bayesian approach to draw a pricing‐error rule on share distribution in EJVs. More specifically, we contend that equity participation by two firms in an EJV allows profit sharing to correct for the errors that they might commit in pricing their inputs to the EJV. For profit sharing to fully nullify such pricing errors, the shares of an EJV must be split between the parent firms in a percentage combination that matches the relative sizes of their pricing errors. Because pricing errors are observable only afterward, share distribution in EJVs resembles a Bayesian process, in which the partners keep updating their estimates on pricing errors to adjust share distribution to a percentage combination that could best nullify their pricing errors. Thus, the eventual outcome of share adjustment is EJV buyout, in that the partner whose pricing errors remain substantial buys out the shares of the other whose pricing errors have become tolerable.
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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 | 0.027 | 0.125 |
| Meta-epidemiology (narrow) | 0.001 | 0.002 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.006 |
| Scholarly communication | 0.006 | 0.011 |
| Open science | 0.005 | 0.004 |
| Research integrity | 0.004 | 0.006 |
| 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".