Bibliographic record
Abstract
Abstract The purpose of this article is to illustrate the growing limitations of the current methods of calculating earnings, particularly when earnings is a negative number. Earnings, presumably the most important output of a financial reporting system, is not a singular metric. It is obtained by subtracting numerous expense line items from revenues, both of which are calculated after applying a diverse, and often inconsistent, set of accounting conventions. Despite this apparent deficiency, earnings could be informative of recurring profits, if revenues are measured correctly and expenses are traced to revenues. However, both principles are increasingly violated for the cohorts of firms listed in the last 30 years, which now constitute over 80% of the set of listed firms. Revenues of recent cohorts do not capture many events that create recurring cash flows. Their operating expenses are dominated by intangible outlays that are unmatched to current revenues. As a result, newer cohorts’ profits and profit margins, especially when negative, offer little to inform future profits. Given that revenue and expense recognition rules are unlikely to change anytime soon, the current developments raise a question: Should the reporting of the summary measure of earnings be voluntary instead of mandatory?
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 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 | 0.001 | 0.026 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".