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Record W2221523760 · doi:10.1111/1911-3838.12086

Boxing and Martial Arts Company, A Tax Case

2015· article· en· W2221523760 on OpenAlexaffvenueabout
Justin Mindzak, Tao Zeng

Bibliographic record

VenueAccounting Perspectives · 2015
Typearticle
Languageen
FieldEconomics, Econometrics and Finance
TopicSports Analytics and Performance
Canadian institutionsWilfrid Laurier University
Fundersnot available
KeywordsMartial artsCorporationRevenueTax revenuePromotion (chess)BusinessFinancePaymentCashEconomicsPublic economicsPolitical scienceLaw

Abstract

fetched live from OpenAlex

Abstract A tax case is developed examining a private Canadian sports company. This case simulates a real‐world start‐up company in the area of Canadian Controlled Private Corporation taxation. The client, Boxing and Martial Arts Co., is a medium‐sized, rapidly growing sports promotion business. It operates in an environment where cash payments are common, especially towards foreign workers. Students must research issues involving tax deductions and revenue recognition. The learning objectives include: reinforcing taxation concepts that most students learn in introductory courses (i.e., revenue recognition, allowable business expenses) as well as improving students’ tax research skills for more complex issues (e.g., international tax treaties).

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 imitation

Not 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.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.002
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.747
Threshold uncertainty score0.508

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.002
Science and technology studies0.0070.003
Scholarly communication0.0030.001
Open science0.0010.001
Research integrity0.0020.001
Insufficient payload (model declined to judge)0.0060.000

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.

Opus teacher head0.055
GPT teacher head0.248
Teacher spread0.194 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designNot applicable
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

Citations1
Published2015
Admission routes3
Has abstractyes

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