A sensory-friendly adaptive concert model supported by caregiver perspectives
Bibliographic record
Abstract
Attending a concert may prove difficult for individuals with exceptionalities or disabilities and those who support them. While traditional performance environments may not feel welcoming or amenable for individuals with exceptionalities and their families, arts organizations have recently made efforts to produce concerts that address barriers to accessibility. These adaptive concerts, most frequently labeled as Sensory-Friendly Concerts, attempt to create environments suitable for diverse communities, supporting individuals and groups who are frequently underrepresented as audience members in performance contexts. This article explores adaptive music performances, contributing a model for sensory-friendly adaptive concerts supported by caregivers’ perspectives through a post-concert survey. The model proposed includes four areas of adaptation: pre-show work, environment audit, extra-musical aids, and programming adjustments. The authors outline the various modifications with data points from a sample of adaptive concert caregiver attendees ( n = 15), aligning the theoretical model with practice to provide practical examples and tangible outputs for researchers, presenters, musicians, educators, and policymakers.
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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 | 0.008 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.004 | 0.006 |
| Scholarly communication | 0.007 | 0.005 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.001 | 0.002 |
| 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".