Bibliographic record
Abstract
In 1951, when Noel Coward penned his mocking ode to the Festival of Britain, Europe was in the throes of a post-war boom in cultural festivals. Coward’s satire points to the precarious status of these events: despite their prominence, they invited ridicule both for their grand cultural claims and their commercial character. As Coward writes, ‘We’ve never been/exactly keen/On showing off or swank/But as they say/That gay display/means money in the bank’ (2002, 343). Like other middlebrow institutions, cultural festivals pursue both artistic and commercial goals and this tension creates confusion about their purpose: in Coward’s phrase, ‘Don’t give anyone time to ask/What the Hell it’s about’ (2002, 345). Literary festivals, a subset of cultural festivals, have existed since 1949, exploding in popularity in recent decades. There are well over 300 literary festivals worldwide, with locations ranging from major international capitals to regional towns. The most established festivals are held in cities from Commonwealth countries such as Toronto, Edinburgh, Adelaide and Melbourne. The twenty-first century has seen the emergence of large literary festivals in America, including the Boston Book Festival and the National Book Festival, and across the globe, from the Jaipur Literature Festival to the Ubud Writers and Readers Festival to the Abu Dhabi International Book Fair. 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 | 0.007 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.024 | 0.021 |
| Scholarly communication | 0.025 | 0.011 |
| Open science | 0.001 | 0.021 |
| Research integrity | 0.002 | 0.005 |
| Insufficient payload (model declined to judge) | 0.019 | 0.004 |
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".