JIT Adoption: The Effects of LIFO Reserves and Financial Reporting and Tax Incentives*
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
Abstract Using matched samples of JIT adopters and nonadopters, we examine the association of JIT adoption with firms' financial reporting and tax incentives, earnings‐management histories, and LIFO reserve levels. We find evidence that adoption decisions are influenced by the interaction of firms' LIFO reserves with their income smoothing, debt covenant, and tax incentives. We also find that adoption is less likely for firms historically engaging in high degrees of earnings management, particularly when such firms have no substantial LIFO reserves. Our study extends earlier research demonstrating a relation between inventory valuation method and year‐end inventory transactions, and documents a relation between earnings‐management incentives and a fundamental supply‐chain design choice.
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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 | 0.012 | 0.043 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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