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Record W2584978043 · doi:10.4324/9781315753461-5

Building Theoretical Foundations For Strategic Csr In Sport 1

2015· book-chapter· en· W2584978043 on OpenAlexaff
Kathy Babiak, Kathryn L. Heinze, Richard Wolfe

Bibliographic record

Venuenot available
Typebook-chapter
Languageen
FieldSocial Sciences
TopicSport and Mega-Event Impacts
Canadian institutionsUniversity of Victoria
Fundersnot available
KeywordsCorporate social responsibilityBusinessPolitical sciencePublic relations

Abstract

fetched live from OpenAlex

In this chapter, the authors suggest that the nature of a professional sport organisation&s;s Corporate Social Responsibility (CSR) efforts depends upon the organisation&s;s focus on external pressures and/or internal resources. Their CSR framework was developed through studying the specific context of professional sport. Their first step in building a framework that would have broad practical and scholarly impact was to scan and review CSR theories and approaches in the academic literature. The authors consider various theoretical perspectives evident in the literature to explain and extend knowledge generated about CSR. In developing their framework, the authors conducted a qualitative study to explore what forces sport practitioners deem central to the adoption and integration of CSR efforts in their organisations. Other extensions and applications of their framing are evident in the work of S. Hamil and S. Morrow, who examined the context and motivation of CSR in the Scottish Premier League.

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)0.006
metaresearch head score (Gemma)0.004
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: Theoretical or conceptual
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.011
Threshold uncertainty score0.069

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0060.004
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0050.004
Science and technology studies0.0050.030
Scholarly communication0.0090.010
Open science0.0020.004
Research integrity0.0040.005
Insufficient payload (model declined to judge)0.0110.001

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 head0.146
GPT teacher head0.400
Teacher spread0.254 · 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 designTheoretical or conceptual
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 routes1
Has abstractyes

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