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Record W1597202001 · doi:10.1142/7822#t=toc

The National Basketball Association: Business, Organization and Strategy

2010· book· en· W1597202001 on OpenAlexaboutno aff
Frank P. Jozsa

Bibliographic record

VenueWorld Scientific Books · 2010
Typebook
Languageen
FieldEconomics, Econometrics and Finance
TopicSports Analytics and Performance
Canadian institutionsnot available
Fundersnot available
KeywordsBasketballAssociation (psychology)BusinessProcess managementGeographyPsychologyArchaeology

Abstract

fetched live from OpenAlex

The National Basketball Association (NBA) is widely recognized as an entertaining and innovative league whose teams play regular season and postseason games in packed arenas at home and away sites in the United States and Canada. This book discusses the development, growth, and success of the 61-year-old NBA from a business perspective. Covering the late 1940s to 2009, it focuses on the league's expansions and mergers, team territories and relocations, franchise organizations and operations, basketball arenas and markets, and NBA domestic and international affairs. Readers will gain an insight into when, how, and why the NBA emerged, reformed, and gradually matured to become one of the world's most dominant, prosperous, and popular professional sports organizations today

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)0.000
metaresearch head score (Gemma)0.001
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: Not applicable
GenreCandidate signal: Other · Consensus signal: Other
Teacher disagreement score0.035
Threshold uncertainty score0.115

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.003
Science and technology studies0.0010.001
Scholarly communication0.0100.004
Open science0.0010.001
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0350.031

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 head0.022
GPT teacher head0.196
Teacher spread0.174 · 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
GenreOther

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

Citations0
Published2010
Admission routes1
Has abstractyes

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