Pirate Wireless: Revenue Recognition in the Telecommunications Industry
Bibliographic record
Abstract
Abstract Pirate Wireless is a telecommunications company with stores and offices all over the globe. Jim Bayley is an extremely busy professional who enters into a contract with Pirate Wireless for the ultimate purchase of a smartphone with one‐year warranty, voice and data services, Cryptonite encryption software, and an extended warranty. The case requires students to act as Assistant Controller of Pirate Wireless Corporate and determine the appropriate revenue recognition for Pirate Wireless's contract with Jim Bayley. This mandates a thorough review of the five steps of revenue recognition set forth in Revenue from Contracts with Customers, the jointly converged standard issued by the Financial Accounting Standards Board (FASB) and the International Accounting Standards Board (IASB) in May 2014. The FASB's Accounting Standards Codification 606 is effective for all U.S. public entities for fiscal years ending after December 15, 2017. The IASB's International Financial Reporting Standards (IFRS) 15 applies to an entity's first annual IFRS financial statements for a period beginning on or after January 1, 2018. This case is appropriate for an undergraduate or graduate level Intermediate Accounting course.
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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 | 0.007 | 0.021 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.008 | 0.007 |
| Scholarly communication | 0.017 | 0.009 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.007 | 0.009 |
| Insufficient payload (model declined to judge) | 0.014 | 0.002 |
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".