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Record W2797047309 · doi:10.1108/aaaj-06-2015-2089

The winding road to fair value accounting in China: a social movement analysis

2018· article· en· W2797047309 on OpenAlexaff
Kathryn Bewley, Cameron Graham, Songlan Peng

Bibliographic record

VenueAccounting Auditing & Accountability Journal · 2018
Typearticle
Languageen
FieldBusiness, Management and Accounting
TopicAuditing, Earnings Management, Governance
Canadian institutionsYork UniversityToronto Metropolitan University
Fundersnot available
KeywordsChinaAccountingOriginalityValue (mathematics)Fair valueEliteBusinessEconomicsSociologyPolitical scienceQualitative researchComputer scienceLaw

Abstract

fetched live from OpenAlex

Purpose The purpose of this paper is to examine China’s stop-start adoption of fair value accounting (FVA) into its national accounting standards. The paper analyzes how FVA standards promoted by transnational organizations were eventually adopted in China despite its conservative accounting traditions. Design/methodology/approach The study uses archival records and an analytic framework adapted from the studies of social movements to identify the institutional factors that differ between China’ first unsuccessful attempt to adopt FVA and its second successful attempt. Findings Shared interests of elite national and international groups, creation of social infrastructure, marshaling of key resources, and specific actions to frame FVA standards are found to be crucial factors supporting FVA reform in China. Practical implications The study helps advance our understanding of dissemination of international accounting regulations in non-Western societies. The findings can help accounting standard setters to avoid costly failures. Originality/value The study provides a structured analysis of the propagation of global accounting regulations. It exposes the factors in the failure and success of FVA adoption in China.

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)0.014
metaresearch head score (Gemma)0.022
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch, Meta-epidemiology (narrow), Science and technology studies, 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 score0.144
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0140.022
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0020.007
Science and technology studies0.0060.000
Scholarly communication0.0060.005
Open science0.0030.002
Research integrity0.0000.002
Insufficient payload (model declined to judge)0.0010.001

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 head0.009
GPT teacher head0.249
Teacher spread0.240 · 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

Citations16
Published2018
Admission routes1
Has abstractyes

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