Bibliographic record
Abstract
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. These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.
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 imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.008 | 0.007 |
| Scholarly communication | 0.008 | 0.003 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.034 | 0.009 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".