Social transformation, collective health and community-based arts: ‘Buen Vivir’ and Ecuador's social circus programme
Bibliographic record
Abstract
Worldwide, interest is increasing in community-based arts to promote social transformation. This study analyzes one such case. Ecuador's government, elected in 2006 after decades of neoliberalism, introduced Buen Vivir ('good living' derived from the Kichwan sumak kawsay), to guide development. Plans included launching a countrywide programme using circus arts as a sociocultural intervention for street-involved youth and other marginalised groups. To examine the complex ways by which such interventions intercede in 'ways of being' at the individual and collective level, we integrated qualitative and quantitative methods to document relationships between programme policies over a 5-year period and transformations in personal growth, social inclusion, social engagement and health-related lifestyles of social circus participants. We also conducted comparisons across programmes and with youth in other community arts. While programmes emphasising social, collective and inclusive pedagogy generated significantly better wellbeing outcomes, economic pressures led to prioritising productive skill-building and performing. Critiques of the government's operationalisation of Buen Vivir, including its ambitious technical goals and pragmatic economic compromising, were mirrored in social circus programmes. However, the programme seeded a grassroots social circus movement. Our study suggests that creative programmes introduced to promote social transformation can indeed contribute significantly to nurturing a culture of collective wellbeing.
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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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| 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".