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Lamborghini brand sharing and cigarette advertising

2017· article· en· W2594592450 on OpenAlexaff
Timothy Dewhirst, Wonkyong Beth Lee

Bibliographic record

VenueTobacco Control · 2017
Typearticle
Languageen
FieldBusiness, Management and Accounting
TopicConsumer Behavior in Brand Consumption and Identification
Canadian institutionsWestern UniversityUniversity of Guelph
FundersJohns Hopkins Bloomberg School of Public HealthHanyang UniversityJohns Hopkins University
KeywordsAdvertisingBusiness

Abstract

fetched live from OpenAlex

Licensing and brand sharing arrangements, where a fee or royalty is paid for use of a name, is a common strategic consideration to provide a newly introduced product or service with an immediate and proven brand identity (table 1).1 Serving as such an example, Korean Tomorrow and Global (KT&G), which is South Korea’s leading tobacco firm, launched a new cigarette brand, Tonino Lamborghini, on 18 April 2012, where the branding resembles the legendary Italian luxury sports car maker (figure 1A,B).2 3 According to The Moodie Report , it took 1 year of negotiation to reach a brand licensing agreement and 3 years to develop the cigarette product.4 The cigarette brand was initially offered in two variants, L8 (predominantly black package) and L6 (predominantly yellow package), with reported tar deliveries of 8.0 mg and 6.0 mg, respectively. Compared with KT&G’s other product offerings, Tonino Lamborghini cigarettes have considerably higher reported tar deliveries and such product characteristics contribute to the brand’s masculine, powerful and assertive image.4–8 Tonino Lamborghini now offers additional brand variants, including ‘Ice Volt’ (figure 2), which is mentholated and has promotional claims that the product possesses ‘the thrilling taste of powerful cooling freshness,’ and ‘Ice Tornado,’ which is described as having a strong, fresh and cool flavour and …

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)0.001
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: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.062
Threshold uncertainty score0.207

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0020.001
Scholarly communication0.0040.002
Open science0.0010.002
Research integrity0.0020.001
Insufficient payload (model declined to judge)0.0620.012

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 head0.023
GPT teacher head0.255
Teacher spread0.232 · 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 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

Citations2
Published2017
Admission routes1
Has abstractyes

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