Bibliographic record
Abstract
The Supreme Court and the Public Company Accounting Oversite Board (PCAOB) has said that an amount is material if there is a substantial likelihood it will influence a reasonable investor’s judgment. The American Institute of Certified Public Accountants (AICPA) has said that an amount is material if there is a substantial likelihood it will influence a reasonable user’s judgment. The Financial Accounting Standards Board (FASB) has refused to define materiality. The Securities and Exchange Commission (SEC) has said that qualitative factors can make even small amounts material. Reasonable implies a consensus of opinion. This article is a meta-analysis of 31,155 materiality decisions made by 335 cohorts in 48 studies with the objective of defining what is reasonable. A cohort is a group of like individuals faced with a common materiality decision. Materiality in this study is measured as a percentage of net income. The mean threshold of materiality is 7.84% and the median is 6.81%. Both thresholds are substantially higher than the often-discussed threshold of 5.0%. A quarter of the participants in these studies set the threshold of materiality at 11.90% and the threshold for a statistically significant difference from the consensus is 17.51%. Ultimately, materiality will be decided through civil and criminal litigation. Finders of fact, usually jurors, will be asked to determine what a reasonable investor would conclude. Few jurors have the training and experience of investors, so without context, they can only guess what a reasonable investor would conclude. This study provides that context.
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 | 0.060 | 0.139 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.015 | 0.053 |
| Bibliometrics | 0.013 | 0.012 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.005 | 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".