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Record W3106233078 · doi:10.1111/1911-3838.12240

What Accountants Need to Know about Blockchain*

2020· article· en· W3106233078 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.
venuePublished in a venue whose home country is Canada.
aboutThe title or abstract carries a Canadian signal from the geographic lexicon.

Bibliographic record

VenueAccounting Perspectives · 2020
Typearticle
Languageen
FieldComputer Science
TopicBlockchain Technology Applications and Security
Canadian institutionsUniversity of Waterloo
Fundersnot available
KeywordsBlockchainLedgerAuditDatabase transactionAccountingSupply chainBusinessDistributed ledgerKnowledge managementFocus (optics)Computer scienceProcess managementMarketingComputer securityDatabase

Abstract

fetched live from OpenAlex

ABSTRACT Reports by professional organizations (e.g., AICPA, CPA Canada, and ICAEW) argue that blockchain adoption is likely to reduce the need for record‐keeping tasks and shift the accounting focus to higher‐level activities. To evaluate such expectations and form their own opinions, accounting professionals need to understand the way blockchain works in a business setting. The primary objective of this article is to provide the reader with the foundational knowledge needed to better understand reports such as those suggesting that blockchain will change the way auditors execute their engagements and generate new assurance opportunities for configuring policies in blockchain networks. Building on evidence indicating that the most promising use cases are from the logistics industry, we describe a blockchain implementation in a supply chain setting. Using a scaffolding approach, we start with a supply chain and describe how blockchain is implemented in one of its segments, before we show how a complete transaction is recorded on the ledger.

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.

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)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.749
Threshold uncertainty score0.909

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0020.001
Research integrity0.0000.000
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 head0.011
GPT teacher head0.254
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