Bibliographic record
Abstract
LIFO (Last in First out) inventory method has been widely used by US publicly traded companies for its tax advantages in many years. However, LIFO is expected to be repealed with the impending acceptance ofIFRS(the International Financial Reporting Standards) by theSEC. The repeal of LIFO will significantly increase the tax liabilities of those companies previously using LIFO. One hardest hit industry by repeal of LIFO is oil industry. Our study investigates the use of LIFO inventory method in oil industry from 2008 through 2015. The primary focus of this study is the accounting information distortion as a result of using LIFO. We document severe accounting information distortion in the areas of working capital and inventory turnover. Though not as severe, we also observe very significant distortions in the areas of gross profit and current ratio. The accounting information gets increasingly distorted from 2008 to 2011. However the trend reverses from 2012 to 2015. Each of the Obama administration’s budgets proposals proposed the elimination of LIFO for inventories. We believe the findings of our research have significant implications for the policy makers. In addition, a full adoption ofIFRS, which prohibits LIFO, is unlikely in the near future. Non-public companies who are not under the jurisdiction of theSECmay still continue to use LIFO after the adoption of IFRS.
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 | 0.004 | 0.021 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.006 | 0.005 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 0.001 |
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".