Bibliographic record
Abstract
IASB plans to publish the fair value measurement standards in the third quarter of 2010.The exposure draft was released in May,2009,and in the following 6 months there were 160 comment letters being received by IASB.Based on these comments,the board held several discussion meetings from January to April in 2010.According to the main content of IASB exposure draft,the feedback of comment letters,and the difference between IASB exposure draft and US GAAP,this paper analyzes the intention underling the standard setting process,the applicable and abusive trade-off arisen by the practice and the impact of the standards on the accounting practice.The paper means to broaden the horizon in this area in China and provide some valuable reference and suggestion for the fair value standards setting and practice.
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 | 0.045 | 0.139 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.007 | 0.005 |
| Science and technology studies | 0.005 | 0.006 |
| Scholarly communication | 0.014 | 0.005 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.016 | 0.017 |
| Insufficient payload (model declined to judge) | 0.017 | 0.017 |
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".