The Power of Social Dance as Social Defiance: Liberation, Identity, and Cohesion
Bibliographic record
Abstract
There is nothing like music and dance to bring people together. However, social dance venues have been criticized for housing socially defiant behavior alongside this group performance. My research project examines the relationship between social defiance and social cohesion as one that is complementary rather than contradictory. Previous research on this topic generally consists of two opposing voices: one argues that these spaces are sites of danger and health risk (e.g. Sanders 2016), and the other argues for their social and cultural importance (e.g. Kavanaugh & Anderson 2008, Hickling & Hutchinson 2012). I uncover a third perspective, arguing that socially divergent behavior increases these events’ capacity to have positive social and cultural impacts. Through extensive bibliographic research, I discovered that when members of an oppressive group engage in socially defiant behavior alongside group performance within social dance spaces, it allows for personal liberation and the construction of individual and group identities. In my presentation for Inquiry@Queen’s, I will reveal this through three case studies: raves (1990s-2000s), Jamaican dancehall (1970s-1980s), and speakeasies (1920s). I selected these case studies because they exemplify “heterotopia spaces” (Foucault 1986) where socially oppressed groups can engage in behavior outside of social norms. In spite of seeming deviant, such contexts offer opportunities for personal liberation from social oppression. Removing the negative lens will expand our view of dance as a powerful tool for social cohesion.
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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 | 0.005 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.014 | 0.095 |
| Scholarly communication | 0.013 | 0.007 |
| Open science | 0.001 | 0.013 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 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".