Assets and Liabilities: When Do They Exist?
Bibliographic record
Abstract
ABSTRACT In this paper, we investigate whether the current references to probability in standard setters' conceptual definitions of assets and liabilities cause individuals to believe that the probability of a future transfer of economic benefits must be above some meaningful threshold for an asset or a liability to exist—a belief that is contrary to standard setters' intent. Results of multiple experiments indicate that the majority of individuals do use a high probability threshold to determine asset existence, whereas for liabilities the majority use a very low threshold. Thus, even under ceteris paribus conditions, liabilities are more frequently judged to exist than assets—a phenomenon analogous to accounting conservatism, as has been discussed in terms of the performance statement. These findings are robust to variation in formal training and in type of liability, and cannot be explained by alternative approaches to judging existence. Consistent with standard setters' intentions, results also suggest that their proposed changes to the definitions of assets and liabilities—changes that attempt to clarify the intended role of probability—do cause a greater proportion of participants to indicate that the relevant financial statement element (asset or liability) exists, relative to participants with no definition. Our study provides important insights for standard setters as they continue work on their missions to update their Conceptual Frameworks and for researchers regarding the role of conservatism on the balance sheet.
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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 | 0.012 | 0.099 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.006 |
| Scholarly communication | 0.004 | 0.008 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| 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".