Discontinued Operations Recognition, Initial Provisions, And Subsequent Adjustments
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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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 | 0.006 | 0.055 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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 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".