Bibliographic record
Abstract
ABSTRACT The centrality of private information in the design of accounting institutions has been explored via agency models that address control concerns as well as disclosure models that amplify valuation issues. This paper derives disclosures by an entrepreneur‐owner when both control and valuation concerns are in play. In particular, the disclosures influence stock price not only via a direct impact on valuation of the firm's revenue but also via an indirect impact on the firm's cost of procuring inputs from a self‐interested and privately informed upstream supplier. In this setting, disclosures are judiciously designed to influence the supplier's decision to share cost information and to control information rents embedded in the procurement contract within the supply chain. Specifically, in order to convey that information rents are not in the offing and, thus, motivate information sharing by the supplier, the owner has incentives to convey a less “rosy” picture. In effect, when controlling supplier actions also becomes important, the owner discloses some unfavorable revenue news that she would have otherwise withheld and conceals some favorable revenue news that she would have otherwise revealed. Consequently, in our model, the disclosure region is either two‐tailed or intermediate, in contrast to the single‐tailed disclosure region implied by familiar valuation considerations alone.
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 | 0.009 | 0.036 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.005 | 0.005 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 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 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".