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Record W2911440817 · doi:10.1111/1911-3838.12167

Pirate Wireless: Revenue Recognition in the Telecommunications Industry

2019· article· en· W2911440817 on OpenAlexvenueno aff
Theresa F. Henry, David P. Mest, Mona L. Safar

Bibliographic record

VenueAccounting Perspectives · 2019
Typearticle
Languageen
FieldBusiness, Management and Accounting
TopicAuditing, Earnings Management, Governance
Canadian institutionsnot available
Fundersnot available
KeywordsRevenueWarrantyRevenue recognitionBusinessAccountingInternational Financial Reporting StandardsFinanceFinancial accountingAccounting information systemLaw

Abstract

fetched live from OpenAlex

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.

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)0.001
metaresearch head score (Gemma)0.003
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.189
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.002
Open science0.0010.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0000.002

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.

Opus teacher head0.016
GPT teacher head0.234
Teacher spread0.218 · 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; a candidate call from one teacher head, not a consensus.

Study designObservational
Domainnot available
GenreEmpirical

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
Published2019
Admission routes1
Has abstractyes

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