QR code-enabled tips to street performers at the Edinburgh Fringe Festival
Bibliographic record
Abstract
Abstract “Hat lines” such as “take one or two pounds out of the wallet…and give me the rest” have been passed down and perfected by generation after generation of street performers. The recent rise of mobile payments has forced them to radically change tack, displaying signage with quick response (QR) codes that lead to cashless tips. We analysed data from the 2022 Edinburgh Fringe Festival to investigate factors that influence the audience use of QR code signs displayed by performers and subsequent tipping activity. We find that what type of act is performing, when and where, and the sign(s) on display are associated with audience activity through QR code scans. Once a code is scanned, we find what type of act is performing, when and where, the sign(s) displayed, and the configuration of the webpage they are directed to are associated with tip activity. Our results provide insights into digital tipping and may help the current generation of street performers, beyond the Edinburgh Fringe, to understand how to incorporate digital payments into their shows and provide insights in other contexts where voluntary donations are sought through scans or tap-to-tip payments terminals.
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 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| 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".