Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
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.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 it