DJs' perspectives on interaction and awareness in nightclubs
Bibliographic record
Abstract
Several researchers have recently proposed technology for crowd-and-DJ interactions in nightclub environments. However, these attempts have not always met with success. In order to design better technologies and systems in this area, it is important to start with an understanding of how nightclub interaction currently happens. To build this understanding, we carried out an interview study focusing on DJ-audience interactions. We interviewed eleven DJs from several different cities, and asked them to discuss the ways that they interact with the audience, and the ways that they maintain and use awareness of the audience. We found that DJs gather a wide variety of information about their audiences, and that this information is important to them as they plan and shape the evening's musical experience. DJs are adept at gathering visual information about the audience, despite poor lighting conditions and a heavy workload of selecting and mixing music. Despite the difficulties, DJs took a dim view of technology designed to let crowds exert more control over the music. This study is one of the first to look closely at the interactive relationship between the DJ and the nightclub audience through the lens of HCI, and our findings provide a number of guidelines for the design of new DJ-focused nightclub technologies.
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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 | 0.004 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.020 | 0.011 |
| Scholarly communication | 0.011 | 0.005 |
| Open science | 0.001 | 0.009 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.007 | 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 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".