Bibliographic record
Abstract
Purpose This paper aims to examine the economic rationale for the shotgun clause, a legally specified protocol for the dissolution of a partnership or a private corporation that empowers one investor to acquire ownership of all the venture's assets. Design/methodology/approach Employing simple mathematics, the behavior of the initiating party or offeror is modeled in a situation of informational asymmetry and then optimized. The implications of offeror optimal behavior are then examined. Findings The paper finds that the introduction of a shotgun clause lowers the offeror's optimal offer price. Whereas in the absence of the clause, the offer price must exceed the offeror's private valuation of the business, in the presence of said clause the offeror's price may not exceed the offeror's private valuation. Situations where the shotgun clause improves versus impairs economic efficiency are delineated. For high (low) offeror private valuations of the business, the shotgun clause induces a greater (lower) discrepancy between said valuation and the offer price. Offerors with high private valuations of the business are shown to prefer the inclusion of the shotgun clause. Practical implications The behavioral ramifications of the shotgun clause are presented, thus providing potential partners and private corporation shareholders a guide for the clause's inclusion or exclusion when the small business is structured. Originality/value This is the first paper to provide an analysis of the shotgun clause in the context of informational asymmetry, employing refinements and simplifications of extant models that address other small business dissolution procedures.
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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 | 0.007 | 0.024 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.007 |
| Scholarly communication | 0.004 | 0.005 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.013 | 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".