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Record W2783457643 · doi:10.1123/cssm.2017-0022

Performance Sports Group: To Invest or Not to Invest

2017· article· en· W2783457643 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.
aboutThe title or abstract carries a Canadian signal from the geographic lexicon.

Bibliographic record

VenueCase Studies in Sport Management · 2017
Typearticle
Languageen
FieldBusiness, Management and Accounting
TopicManagement and Marketing Education
Canadian institutionsUniversity of Windsor
Fundersnot available
KeywordsNoticeInheritance (genetic algorithm)Stock (firearms)BusinessMarketingCommerceEngineeringLawPolitical science

Abstract

fetched live from OpenAlex

In August of 2015, Felix Farmer received notice that he would be inheriting a large sum of money from his great-uncle’s will. Farmer is contemplating investing $50,000 CAD ($38,251 USD) of his inheritance in the parent company of his favorite hockey brand, Bauer. Performance Sports Group (PSG) is a leading manufacturer in the global sporting goods industry that is publicly traded on both the Toronto and New York Stock exchanges, and the parent of such highly successful brands as Bauer and Easton. This case study challenges students to calculate financial ratios, apply various other financial analyses to understand the financial performance of PSG, and complete a Porter’s (2008) Five Forces industry analysis as a means of deciding whether Farmer should invest a portion of his inheritance with PSG.

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.

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)0.002
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.237
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0010.000
Scholarly communication0.0000.001
Open science0.0010.002
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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 head0.059
GPT teacher head0.324
Teacher spread0.264 · 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