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Record W2766147726 · doi:10.1080/16081625.2017.1392877

Classification shifting within non-recurring items

2017· article· en· W2766147726 on OpenAlexfundno aff
Masako N. Darrough, Yong Gyu Lee, Hyung Il Oh

Bibliographic record

VenueAsia-Pacific Journal of Accounting & Economics · 2017
Typearticle
Languageen
FieldBusiness, Management and Accounting
TopicAuditing, Earnings Management, Governance
Canadian institutionsnot available
FundersSungkyunkwan UniversityKorea UniversityParkinson Society CanadaKorea Advanced Institute of Science and TechnologyCity University of New York
KeywordsIncentiveEquity (law)BusinessIncome statementAsset (computer security)Statement (logic)EconomicsActuarial scienceFinanceBalance sheetMicroeconomicsPolitical scienceComputer science

Abstract

fetched live from OpenAlex

This study examines whether managers shift income-decreasing special items to discontinued operations. We expect managers to engage in this form of classification shifting because discontinued operations are reported below income before extraordinary items and discontinued operations (IBXD) on the income statement. Consistent with this expectation, we find evidence suggesting that managers classification-shift asset write-downs to discontinued operations. Furthermore, we find that classification shifting from asset write-downs to discontinued operations is driven by firms with incentives to avoid reporting a negative IBXD and by firms with equity market benefits to classification shifting.

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 distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.003
metaresearch head score (Gemma)0.007
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Scholarly communication
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.306
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.007
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0010.000
Scholarly communication0.0030.007
Open science0.0020.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.017
GPT teacher head0.226
Teacher spread0.209 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations8
Published2017
Admission routes1
Has abstractyes

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