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Using Mini-Cases to Develop AICPA Core Competencies

2015· book-chapter· en· W2500054128 on OpenAlexfundno aff
Vincent C. Brenner, Monica M. Jeancola, Ann L. Watkins

Bibliographic record

Venuenot available
Typebook-chapter
Languageen
FieldBusiness, Management and Accounting
TopicAccounting Education and Careers
Canadian institutionsnot available
FundersMcMaster University
KeywordsCore competencyAccountingFinancial accountingCore (optical fiber)PsychologyMedical educationBusinessAccounting information systemComputer scienceMedicineMarketing

Abstract

fetched live from OpenAlex

Abstract Purpose The subject area of the assignment is financial accounting and AICPA core competency skill development. This instructional tool enhances coverage of financial accounting topics in undergraduate Intermediate Accounting courses and graduate level Financial Accounting courses. Methodology/approach This paper provides a series of mini-cases which can be assigned to students to complete either in writing, through a brief presentation or both. Assignments can be completed on an individual basis or as a group. This provides flexibility for targeting different skill sets. Findings Mini-cases are short and less time-consuming than traditional cases, so instructors can use multiple assignments with different formats in a single semester. This provides students the opportunity to improve skills over a number of assignments within a semester. Practical implications A list of supplementary materials is made available and includes sample mini-cases, sample search results from the AICPA Codification, and sample memorandums. Originality/value The mini-cases provided in this paper are designed to facilitate the development of AICPA core competencies. This includes communication and leadership skills, strategic and critical thinking skills, problem solving, anticipating and serving evolving needs, synthesizing intelligence to insight, and integration and collaboration.

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)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Insufficient payload (model declined to judge)
Consensus categoriesInsufficient payload (model declined to judge)
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Other · Consensus signal: Other
Teacher disagreement score0.303
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0040.004

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 head0.184
GPT teacher head0.299
Teacher spread0.115 · 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; both teacher heads agree on what is shown here.

Study designNot applicable
Domainnot available
GenreOther

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

Citations6
Published2015
Admission routes1
Has abstractyes

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