Classifications Manipulation and Nash Accounting Standards
Why is this work in the frame?
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Full frame distilled prediction
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.
- Candidate categories
- Metaresearch, Scholarly communication
- Consensus categories
- none
- Domain
- Candidate signal: noneConsensus signal: none
- Study design
- Candidate signal: Not applicableConsensus signal: none
- Genre
- Candidate signal: EmpiricalConsensus signal: Empirical
- Teacher disagreement score
- 0.612
- Threshold uncertainty score
- 0.999
- Validation status
machine_predicted_unvalidated·codex-gemma-dda1882f352a
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.007 | 0.022 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.004 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
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.
- Teacher spread
- 0.259 · how far apart the two teachers sit on this one work
- Validation status
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
Abstract
This paper studies a model of “classifications manipulation” in which accounting reports consist of one of two binary classifications, preparers of accounting reports prefer one classification over the other, an accounting standard designates the official requirements that have to be met to receive the preferred classification, and preparers may engage in “classifications manipulation” in order to receive their preferred accounting classification. The possibility of classifications manipulation creates a distinction between the official classification described in the statement of the accounting standard and the de facto classification, determined by the “shadow standard” actually adopted by preparers. The paper studies the selection and evolution of accounting standards in this context. Among other things, the paper evaluates “efficient” accounting standards, it determines when there will be “standards creep,” it introduces and analyzes the notion of a Nash accounting standard, and it compares the standards set by sophisticated standard–setters to those set with less knowledge of firms’ financial reporting environments.
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.
The record
- Venue
- Journal of Accounting Research
- Topic
- Auditing, Earnings Management, Governance
- Field
- Business, Management and Accounting
- Canadian institutions
- Kellogg's (Canada)
- Funders
- not available
- Keywords
- AccountingContext (archaeology)Set (abstract data type)Financial statementAccounting standardDe factoAccounting researchAccounting information systemOrder (exchange)BusinessShadow (psychology)Financial accountingComputer scienceFinanceAuditPolitical sciencePsychology
- Has abstract in OpenAlex
- yes