Arachnophobia: A Case on Impairment and Accounting Ethics
Bibliographic record
Abstract
ABSTRACT This case requires students to apply accounting and ethical decision making within the context of a potential land impairment decision. Students are required to research the relevant professional literature and provide appropriate FASB codification references and IAS cites as they investigate the significant uncertainties that frequently are associated with valuation and impairment analyses. Students also are required to evaluate the ethical implications of the decisions that could be made regarding the necessity of impairment. The case provides an opportunity for students to extend their research and financial accounting abilities, to consider the consequences associated with a set of potentially reasonable accounting alternatives, and to begin to appreciate how the significant uncertainties that are present in many accounting and auditing situations require consistent technical and ethical decision making. The case could be used in Intermediate Accounting I, as well as in undergraduate and graduate Auditing or Ethics classes.
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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 | 0.002 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.007 | 0.014 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.007 | 0.007 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
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.
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".