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Record W2937173235 · doi:10.1123/cssm.2018-0008

To Grow or Not to Grow: Strategy Development at PGC Basketball

2019· article· en· W2937173235 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.

Bibliographic record

VenueCase Studies in Sport Management · 2019
Typearticle
Languageen
FieldSocial Sciences
TopicSport and Mega-Event Impacts
Canadian institutionsUniversity of Waterloo
Fundersnot available
KeywordsBasketballGuard (computer science)Public relationsQuality (philosophy)ManagementAdvertisingPolitical sciencePsychologyMarketingBusinessHistoryComputer scienceEconomics

Abstract

fetched live from OpenAlex

Mano Watsa, President of Point Guard College (PGC) Basketball, is contemplating the next direction to take his organization. His co-owner, Nicole, is adamant that the next five years should be focused on growing PGC Basketball. Like Nicole, Mano would love to see PGC Basketball continue to grow; however, he is skeptical about focusing on growth when the organization is facing some significant challenges. Specifically, PGC Basketball is faced with a low athlete annual retention rate (i.e., 20%) and camps in some regions operating below 70% capacity. In addition, Mano recognizes that PGC Basketball has issues achieving consistency within their operations to ensure quality control, promoting their summer camps within all the markets they serve, as well as attracting and retaining top talent to work as camp instructors. Mano must determine the best strategy to implement for PGC Basketball to continue its success over the next five years.

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)0.001
metaresearch head score (Gemma)0.000
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: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.727
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.001

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.

Opus teacher head0.098
GPT teacher head0.396
Teacher spread0.298 · 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