The Same Difference? A Transfer-Pricing Case
Bibliographic record
Abstract
This case requires you to consider the complexities of transfer pricing. The case is based on an actual situation that occurred between a customer sales representative and a client at an auto dealership. In this case, you will be asked to assume several different roles and attempt to resolve transfer-pricing issues for which there are no “clear-cut” solutions. This case includes three sections. First, you will read assigned background material on transfer pricing, read a short introduction to the case, and become familiarized, through class discussion, with how auto dealerships operate with respect to new and used car sales. Second, you will analyze additional information so that you may assume a particular role, such as new car manager or used car manager. Third, you will assume the role of the newly appointed controller of the dealership and attempt to address and resolve the transfer-pricing issues in this case. By completing this case, you will develop an understanding of alternative transfer-pricing policies (market price, acquisition cost, negotiated price) and the impact of transfer-pricing policies on various parties, as well as come to appreciate how bonus structures may influence managerial decision making.
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 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 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.010 | 0.010 |
| Scholarly communication | 0.008 | 0.012 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.014 | 0.010 |
| Insufficient payload (model declined to judge) | 0.017 | 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".