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Record W4280493516 · doi:10.4324/9781003111849

Stadia Naming Rights in Sport

2022· book· en· W4280493516 on OpenAlexaff
Leah Gillooly, Terry Eddy, Dominic Medway

Bibliographic record

Venuenot available
Typebook
Languageen
FieldSocial Sciences
TopicDoping in Sports
Canadian institutionsUniversity of Windsor
Fundersnot available
KeywordsPolitical science

Abstract

fetched live from OpenAlex

This book is an accessible, practical, and systematic guide to stadium naming rights sponsorship within sport, designed to help practitioners and students gain a better understanding of how naming rights work and the benefits that sport and corporate organisations may get from this kind of arrangement.  The book explains the key principles underpinning naming rights deals and sports sponsorship in non-specialist language for readers with little prior knowledge of the subject. Drawing on examples and case studies of naming rights sponsorships in international markets, across both professional and amateur sport, the book examines key practical issues such as how naming rights differ from other types of sponsorship, why brands should sign a naming rights deal, and how organisations can maximise their return on naming rights sponsorship.  Concise, informative, and practice-focused, this book offers essential insights for all sport management practitioners, for any marketing executives considering sport sponsorship, and for any students or researchers with an interest in sport marketing, sport management, marketing, or events and facilities management.

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)0.000
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: Other · Consensus signal: Other
Teacher disagreement score0.849
Threshold uncertainty score0.990

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0210.000

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 head0.017
GPT teacher head0.291
Teacher spread0.275 · 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 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

Citations1
Published2022
Admission routes1
Has abstractyes

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