Bibliographic record
Abstract
The COVID-19 global pandemic forced most nightlife venues to shut their doors in March 2020, leading to a loss of employment for nighttime employees and freelancers as well as a loss of revenue for the city. As night clubs shut down, the social dancers who fuel this part of the nightlife economy lost access to the spaces where they dance with others who share their musical tastes. Yet seedlings can spring up even in burned over territory. In the face of these pandemic challenges, the dance music scene reinvented itself, shifting from existing in-person to entirely virtual performance. This reimagination of nightlife points to a key element in the resilience of night-time social dancing: community. These virtual dance parties stemmed from, and perpetuated dance communities that replaced, and in some cases redefined, the experiences that dance communities formerly enjoyed in in-person venues. This paper explores this world of virtual dancing. Through conversations with venue owners, performers, and social dancers, as well as through a digital ethnography of virtual dance parties and their corresponding social media pages, this study asks whether and how virtual dance parties replicate the sense of community experienced in in-person dance parties and interrogates what the advent of virtual dance parties means for the future of urban nightlife. Building on the idea that social dancing is a right to the city (Krisel 2020; see also Harvey 2008; Lefebvre 1996), this study also explores how social dancing may also be a right to the internet and explores the parallel between the urban and internet environments as venues where subcultures can form communities and co-create both physical and virtual spaces.
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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 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".