The effect of affect: The role of affective atmosphere for community music practitioners
Bibliographic record
Abstract
There are many things stirring within a given community music event. As practitioners, how do we look beyond our planned outcomes to access the unplanned, the unexpected or, as Lee Higgins terms it, the impossible? Understanding affect theory as the social, cultural and psychological manifestation of reactions and emotions arising from encounters between subjects and people, and between people and objects in the environment, this article discusses the value of this theoretical framework to uncover a deeper understanding of the interactions and responses from participants in a community music event. Using a case study of the organization KW Junk Music, I interviewed participants of three junk music events in Kitchener, ON, Canada. The lens of affective atmosphere, described as the atmosphere produced as a result of the intermingling of affects, emotions and sensations within a given space, provides an informative perspective through which to acknowledge the complexities surrounding all stages of a community music event. I argue that the affective atmosphere that emerges out of each event, both by design and by happenstance, has the potential for change, agency and transformation.
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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 | 0.009 | 0.016 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.006 | 0.014 |
| Scholarly communication | 0.010 | 0.004 |
| Open science | 0.001 | 0.007 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".