Discontinued Operations Recognition, Initial Provisions, And Subsequent Adjustments
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
This study extends our understanding of why firms choose to take discretionary write-offs and identifies factors that influence the measurement of the charges taken. We focus on segment disposals, initial provisions recorded upon discontinuance of those segments, and adjustments to initial provisions that accompany the segment disposals. We partition our sample into those disposals that were substantially completed at the time of recognition (nondiscretionary disposals) and those that were recognized prior to disposal completion (discretionary disposals). With respect to motivations for taking discretionary rather than nondiscretionary disposals, we find that firms electing discretionary disposals discontinue segments that experience sharp declines in earnings and that require more negative initial provisions; the continuing portion of these firms are less profitable and are in weaker financial condition when compared to firms recognizing disposals upon completion. Further, they are more likely to announce the disposal in the fourth quarter, and they are more likely to underestimate the cost of disposal. With respect to measurement issues, we find that subsequent adjustments to initial provisions for discretionary disposals relate both to firms’ abilities to estimate losses on disposal at the plan date and to management incentives to manage disclosures. In contrast, subsequent adjustments accompanying nondiscretionary disposals relate primarily to uncertainties contained in the disposal agreement.
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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