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Record W4283171390 · doi:10.1111/1911-3846.12800

How Accounting Ends: <scp>Self‐Undermining</scp> Repetition in Accounting Life Cycles*

2022· article· en· W4283171390 on OpenAlexvenueno aff
Tommaso Palermo, Michael Power, Simon Ashby

Bibliographic record

VenueContemporary Accounting Research · 2022
Typearticle
Languageen
FieldBusiness, Management and Accounting
TopicAccounting and Organizational Management
Canadian institutionsnot available
Fundersnot available
KeywordsRepetition (rhetorical device)AccountingAmbiguityPsychologyComparabilitySocial psychologyBusinessComputer scienceMathematics

Abstract

fetched live from OpenAlex

ABSTRACT This study develops a process model of how accounting may come to an end. Grounded in a longitudinal study of a risk culture survey, this model focuses on the dynamics that underpin the repetition of accounting practices, and sheds light on two boundary conditions of successful repetition and continuation, which are in tension with each other. On the one hand, there are pressures for repetition that preserves continuity and comparability. On the other hand, there is the ongoing organizational need to adjust accounting practices. Iterating between the case study findings, social studies of accounting, and the sociology of replication in scientific practice, the model shows how moving too close to either boundary increases the risk that repetition undermines the accounting practice being repeated: “perfect repetition” may be perceived as uninteresting and decision‐irrelevant; very “imperfect repetition” may be perceived as something too different and idiosyncratic, and hence also decision‐irrelevant. As a result, the analysis extends a rich literature that has examined empirical instances of failure of the conditions that sustain the repeatability of accounting practices. Via the theory of “self‐undermining repetition,” this study shows how the possibilities for accounting's ending are paradoxically inherent in the very act of repetition. This notion of “self‐undermining repetition” is deepened by a discussion of how it may be affected by four contingencies: task ambiguity, organizational politics, organizational actors' reflexivity, and external networks of support. Overall, the analysis of the self‐undermining dynamics of repetition and related contingencies contrasts with research that foregrounds the constitutive nature of repeated uses of accountings. It shows how repetition may also undermine, rather than cumulatively consolidate, accounting practices.

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)0.013
metaresearch head score (Gemma)0.008
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch, Meta-epidemiology (narrow), Science and technology studies, Scholarly communication, Research integrity
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.715
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0130.008
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0040.007
Science and technology studies0.0040.000
Scholarly communication0.0050.008
Open science0.0020.005
Research integrity0.0000.003
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 head0.053
GPT teacher head0.281
Teacher spread0.228 · 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 designNot applicable
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

Citations18
Published2022
Admission routes1
Has abstractyes

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