Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
The purpose of our study was to examine how hacking – as discussed and displayed by participants of Monthly Music Hackathon NYC – could inform making music education practices more accessible and inclusive, if at all, for people with disabilities. Free and open to the public, Monthly Music Hackathon NYC hosts non-competitive community-based events in which participants – musicians, educators, coders, and software/hardware designers from beginner to expert – work on projects collaboratively over the course of a day to address real-world problems posed by stakeholders in their communities. Our research team consisting of the principal investigator and two research assistants attended and videorecorded the events of Monthly Music Hackathon NYC’s ‘Music AccessAbility’ hackathon. In this article, we detail what constitutes hacking at this event and how participants approached hacking disability. We discuss the potential of hacking in music education to create a more accessible and inclusive field, and the importance of championing a disability-led design model as the ethical way forward.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it