Ted's Teas: A Two‐Part Accounting and Audit “Crossover Case”*
Bibliographic record
Abstract
Abstract This fictional case is based on a real specialty tea company in Nova Scotia. Ted's Teas is a two‐part crossover case that illustrates the integration of financial accounting and audit learning outcomes applied to real‐world scenarios. In Part 1, students assume the role of an internal accountant and apply knowledge of the Chartered Professional Accountants (CPA) of Canada's CPA Way to identify how to treat leases, changes in policy, and estimates as part of accounting knowledge under both Accounting Standards for Private Enterprises and IFRS frameworks. In Part 2, students “cross over” and change roles, so they are now external auditors for Ted's Teas, tasked to provide an analysis of risk of material misstatement, recommend an audit approach, and develop substantive procedures. This two‐part case presents opportunities for students to demonstrate technical competence in multiple areas, either separately in two different courses or combined in one “capstone” or case course. The case and teaching notes, including the marking rubrics, were adapted from principles used to train and evaluate CPA Professional Education Program candidates, tailored to an appropriate level for undergraduate and graduate learners.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.006 | 0.004 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.012 | 0.001 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".