Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
The attempt of the Financial Accounting Standards Board (FASB) to provide consistent and logical accounting treatment for hedging transactions, in the form of FAS 133, continues to generate controversy. Under the new rules, many kinds of hedges do not cause problems, but a variety of difficulties still remain. One of the more significant ones involves how the changes in the current value of an option used in a hedge are to be treated. In some cases, changes in an option9s time value are supposed to enter into earnings, which can produce spurious earnings volatility during the period of a hedge. Bodurtha and Thornton describe the situation under FAS 133 and then propose two possible solutions. One is to suggest a change in the rule that would remove the problem. Their “financial engineering solution” would not depend on a rule change, but would introduce new types of derivatives whose contingent cash flows could be organized in such a way as to qualify for hedge treatment under the current rules, but would remove the artificial earnings volatility that would otherwise occur with a standard option hedge.
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.
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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.002 |
| Open science | 0.000 | 0.000 |
| 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 it