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Record W4205089200 · doi:10.1123/cssm.2020-0025

Hey Alexa, Launch Twitch: Using Sport Sponsorship to Drive Business Development

2021· article· en· W4205089200 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 · 2021
Typearticle
Languageen
FieldSocial Sciences
TopicDigital Games and Media
Canadian institutionsBrock University
Fundersnot available
KeywordsPopularityBusiness planPlan (archaeology)AppealBusinessAmazon rainforestMarketingAdvertisingBusiness modelMiddle EastValue (mathematics)Political scienceComputer scienceGeography

Abstract

fetched live from OpenAlex

In this case study, students will explore how sport sponsorship can be used to drive business development. They will follow the fictitious story of Amazon, developing a plan to expand its operations into the Middle East through the eSports platform Twitch. Twitch, a video game livestreaming site has contributed to the rise popularity of eSports. Thanks to its appeal to the youth demographic, it is revealed Twitch offers a unique platform that can give Amazon a competitive advantage. This aligns with the Middle East’s increasing interest in becoming a global sport leader. After further exploring the Middle East market, the potential value of this sponsorship will be determined. In addition, business-to-consumer strategies will be consulted to justify the plan put forward by Amazon. Learning objectives include understanding the role of new media and being able to understand the early phases of a sponsorship plan.

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 categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.883
Threshold uncertainty score0.882

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)0.0000.000

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 head0.094
GPT teacher head0.369
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