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Record W2159200128 · doi:10.1506/5k7c-yt1h-0g32-90k0

Competency‐Based Education and Assessment for the Accounting Profession: A Critical Review*

2003· article· en· W2159200128 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.

Bibliographic record

VenueCanadian Accounting Perspectives · 2003
Typearticle
Languageen
FieldBusiness, Management and Accounting
TopicAccounting Education and Careers
Canadian institutionsUniversity of Waterloo
Fundersnot available
KeywordsAppealVariety (cybernetics)Strengths and weaknessesVisionValue (mathematics)Competency assessmentAccountingKnowledge managementBusinessEngineering ethicsPsychologyPolitical scienceComputer scienceMedical educationSociologyMedicineEngineering

Abstract

fetched live from OpenAlex

ABSTRACT In recent years many professional accounting associations have become interested in establishing competency‐based professional requirements and assessment methods for certifying accounting professionals. A competency‐based approach to qualification specifies expectations in terms of outcomes, or what an individual can accomplish, rather than in terms of an individual's knowledge or capabilities. This idea has an obvious appeal to many practitioners and administrators of professional qualification programs. However, there is limited knowledge about competency‐based approaches in the accounting profession and among accounting academics, which is constraining discussion about the value of these approaches and about the strengths and weaknesses of the different competency models that have sprung up in various jurisdictions. In this paper we review and synthesize the literature on competency‐based approaches. We identify a number of theoretical benefits of competency‐based approaches. However, we also find many alternative definitions and philosophies underlying competency‐based approaches, and a variety of visions of how competencies should be determined and assessed. We note that there is limited evidence supporting many competency‐based approaches and we identify 14 research questions that could be used to help policy makers to more effectively address policy matters related to competency‐based education and assessment.

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)0.001
metaresearch head score (Gemma)0.003
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesScience and technology studies
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.904
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0010.000
Scholarly communication0.0010.001
Open science0.0000.000
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 head0.022
GPT teacher head0.318
Teacher spread0.296 · 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