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Record W2956039920 · doi:10.5430/ijba.v10n4p64

Social Marketing for Restraining the Violence of the Supporters by Behaviour Change

2019· article· en· W2956039920 on OpenAlexvenueno aff
Edson Coutinho da Silva, Alexandre Luzzi Las Casas

Bibliographic record

VenueInternational Journal of Business Administration · 2019
Typearticle
Languageen
FieldBusiness, Management and Accounting
TopicCustomer Service Quality and Loyalty
Canadian institutionsnot available
Fundersnot available
KeywordsStadiumSocial marketingMarketingPublic relationsSports marketingClubQuality (philosophy)General partnershipSociologySport managementAdvertisingBusinessRelationship marketingMarketing managementPolitical science

Abstract

fetched live from OpenAlex

This theoretical paper aims to introduce and discuss the role of the social marketing as a tool to decrease the index of violence between supporters and improve the satisfaction, well-being and quality of life of fans (as a whole) in the stadium. Understanding the violence in the stadium as a social problem; social marketing becomes a relevant instrument to decrease the violence between supporters since behaviour change is the core concept. Social marketing principles use ideas to transform a social scenario. Social marketing seeks, in the sports area, to encourage supporters to perform an active role in the well-being process in the stadium, taking into consideration themselves, sports club, public services preservation and non-supporters. The social marketing campaign should be designed by a public organisation using the partnership with the sports clubs or sponsorships to improve the offer of well-being for individuals; however, not providing profits for anyone.

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 categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.151
Threshold uncertainty score0.238

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 communication0.0000.001
Open science0.0010.000
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 head0.040
GPT teacher head0.298
Teacher spread0.259 · 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.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
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

Citations0
Published2019
Admission routes1
Has abstractyes

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