Bibliographic record
Abstract
This chapter focuses on the UK's biggest and most influential festival, the Edinburgh Festival Fringe (EFF), analyzing its benefits and risks. It considers some of the EFF's advantages: the opportunities for artists to do a three-week run, to build relationships with other artists, and take part in an international hothouse for seeing work, learning, and developing. The chapter also considers the EFF's pernicious effects: its unregulated labour conditions; environmental impact; lack of integration into Edinburgh's year-round performance culture; economic and cultural exclusiveness; competitive individualization of success and failure; and pressures on mental health. It ends by proposing ways the EFF and its emulators could improve their social impact by investing in infrastructure, Edinburgh's performance culture, and performance makers; actively supporting artists' mental health; offering structural mentoring support; introducing regulations that protect workers; actively supporting more diverse makers, critics and audiences; and advocating for collaboration over competition. The chapter advocates for a vision of the fringe as, not a neo-liberal capitalist market, but a civic sphere.
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 imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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.
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 teacher head, 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".