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Record W4232079989 · doi:10.1007/978-1-137-58316-1_9

Sponsoring Shakespeare

2016· book-chapter· en· W4232079989 on OpenAlexaboutno aff
Susan Bennett

Bibliographic record

VenuePalgrave Macmillan UK eBooks · 2016
Typebook-chapter
Languageen
FieldArts and Humanities
TopicShakespeare, Adaptation, and Literary Criticism
Canadian institutionsnot available
Fundersnot available
KeywordsOlympiadValue (mathematics)The artsContext (archaeology)TourismExpansiveAdvertisingPopularityArtSociologyMedia studiesPolitical scienceHistoryVisual artsLawBusiness

Abstract

fetched live from OpenAlex

There is, by now, a long critical history of how Shakespeare has been appropriated and performed in advertising campaigns for a remarkable diversity of consumer products, from StarKist canned tuna to easyJet’s low-cost air travel, from Red Bull energy drinks to Google+.1 Even more commonplace is the analysis of how the Bard is deployed to promote cultural institutions and places involved directly in the production of his works. In this context, Shakespeare’s role in cultural tourism has been particularly well documented, and not just in the obvious locations of Stratford-upon-Avon and London, but in festival cities such as Stratford, Ontario and Ashland, Oregon.2 Against the backdrop of this expansive Shakespeare ‘industry’, Kate McLuskie and Kate Rumbold have explored whether Shakespeare can rightly be considered a ‘brand’, suggesting that what is at stake ‘is the question of how Shakespeare’s value is constructed and conferred in commercial settings’.3 I am interested here in taking up the notion of value in practices of corporate sponsorship. In particular, I want to explore how value was assumed and attained by Shakespeare’s presence in the Cultural Olympiad attached to London 2012 and, specifically, the relationship of his ‘brand’ to corporate sponsors for both the arts programming and the larger sporting event.

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)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Insufficient payload (model declined to judge)
Consensus categoriesInsufficient payload (model declined to judge)
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: Theoretical or conceptual
GenreCandidate signal: Other · Consensus signal: none
Teacher disagreement score0.569
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0240.002

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 head0.226
Teacher spread0.186 · 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; both teacher heads agree on what is shown here.

Study designTheoretical or conceptual
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
Published2016
Admission routes1
Has abstractyes

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