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Record W3170245693 · doi:10.1111/1911-3838.12265

To Be or Not to Be: Blockchain and the Future of Accounting and Auditing<sup>*</sup>

2021· article· en· W3170245693 on OpenAlexaffvenue
Olivier Desplebin, Gulliver Lux, Nicolas Petit

Bibliographic record

VenueAccounting Perspectives · 2021
Typearticle
Languageen
FieldComputer Science
TopicBlockchain Technology Applications and Security
Canadian institutionsUniversité du Québec à Montréal
Fundersnot available
KeywordsBlockchainAuditAccountingBusinessSoftware deploymentAccounting information systemWork (physics)Function (biology)Computer scienceEngineeringComputer security

Abstract

fetched live from OpenAlex

ABSTRACT The purpose of this article is to study the potential impact of blockchain technology on accounting systems and businesses. Blockchain technology characteristics and operating modes have the potential to bring about innovations to the fields of accounting and auditing. After describing the function of blockchain technology, we examine potential accounting transformations and identify how the deployment of this technology might impact accountants and external auditors. To that end, we review the literature and take a prospective approach to the use of blockchain in accounting and auditing. Three major issues regarding the future of blockchain in accounting and auditing emerge: (i) the transformation of accounting techniques; (ii) main evolutions in accounting and auditing; and (iii) main evolutions in the work, skills, and education of auditors.

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 machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.002
metaresearch head score (Gemma)0.004
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.005
Threshold uncertainty score0.021

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.004
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.002
Science and technology studies0.0010.004
Scholarly communication0.0050.005
Open science0.0000.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0050.001

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 head0.010
GPT teacher head0.253
Teacher spread0.243 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
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

Citations71
Published2021
Admission routes2
Has abstractyes

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