Experiments with Book Festival People (Real and Imaginary)
Bibliographic record
Abstract
While there are multiple approaches to researching cultural events, predominant academic frames tend to be either sociological or situated within a creative industries discourse. Neither of these approaches have supported sustained engagement with individual, interior experience at book festivals. Creative writers have imaginatively depicted these sites of author-reader interaction, and developing scholarship focuses on autoethnography and the phenomenological. In this article, we extend and materialise these approaches through a series of creative, arts-informed interventions: @AuthorsYurt, a personification on Twitter of the Edinburgh International Book Festival’s green room; Paper Dolls, a series of cut-out-and-dress dolls depicting audience members at a variety of book festivals across Europe, North America and Australia; and ClueButeDo, a satirical reworking of the audience feedback form at a small island crime festival in the UK. Each of the three experiments reveals aspects of personhood at book festivals, engaging with ideas of interiority, individuality, and experientiality, as well as of inclusion and exclusion. In pursuing this aim, we are guided by the autoethnographic slogan, “No Insight Without Inside, No Inside Without Outside” (Nunu Otot).
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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 | 0.008 | 0.016 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.005 | 0.004 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.003 | 0.005 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.017 | 0.003 |
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".