Assessing accessibility: an instrumental case study of a community music group
Bibliographic record
Abstract
Creating accessible events is a pressing issue for many music organisations. In the United States, the term accessibility has strong ties to disability, and it is an important concept because what is deemed accessible directly impacts who is included and excluded from music experiences. Music Community Lab (MCL) runs a series of events in New York City called Monthly Music Hackathon NYC. They aim to promote ‘diversity across backgrounds, perspectives, and abilities’. This instrumental case study sought to examine how MCL participants conceptualise accessibility as well as analyze participants’ suggestions for improving the accessibility of MCL events. Sixty-two people who attended one of three MCL events completed a demographic survey and 57 of those respondents participated in an interview. Findings reveal that 63% (n = 36) of participants associated accessibility with inclusivity and 35% (n = 20) of participants associated accessibility with ease of access to resources, places, and experiences. Participants’ suggestions for improving accessibility included social media marketing (n = 23; 40%) and ease of access approaches (n = 11; 19%) including CART, ASL, and live streaming events. Accessibility is challenging for community music groups like MCL to navigate because it is a complex construct with varied interpretations.
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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 | 0.007 | 0.017 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.018 | 0.007 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.003 | 0.009 |
| Research integrity | 0.004 | 0.004 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".