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Record W4417252675 · doi:10.2308/api-2024-017

Communicating Norms of Conduct: The Semantics of Professionalism

2025· article· en· W4417252675 on OpenAlexaffabout
Minqi Liu, Kieran Taylor-Neu, Gregory D. Saxton

Bibliographic record

VenueAccounting and the Public Interest · 2025
Typearticle
Languageen
FieldBusiness, Management and Accounting
TopicAccounting Education and Careers
Canadian institutionsUniversity of AlbertaYork University
Fundersnot available
KeywordsNormativeSemantics (computer science)Association (psychology)CertificationFocus (optics)Key (lock)Public accounting

Abstract

fetched live from OpenAlex

ABSTRACT This paper explores how two public accounting associations, the American Institute of Certified Public Accountants (AICPA) and the Canadian Institute of Chartered Accountants (CICA), conveyed norms of conduct to members and students through editorials published in the Journal of Accountancy and CA Magazine from 1916 to 1973, with a focus on the use of profession-related terms. Drawing on Bakhtin’s concept of stylistic aura and employing advanced textual analysis techniques, the study reveals that profession stem words played a key role in communicating these norms by synthesizing varied normative concerns into a cohesive professional discourse applicable across settings. The semantic meanings of these sentences shifted over time and between journals. Additionally, the study finds that utterances with profession stem words were more pervasive and better at highlighting competing concerns than ethics stem words. This study contributes to our understanding of accounting association communication processes, specifically communication via professional narratives.

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)0.002
metaresearch head score (Gemma)0.001
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 score0.846
Threshold uncertainty score0.288

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0010.001
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 head0.055
GPT teacher head0.300
Teacher spread0.245 · 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.

The models applied no category: nothing in the taxonomy fit this work.
Study designTheoretical or conceptual
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

Citations0
Published2025
Admission routes2
Has abstractyes

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