Field to Media: applied ecomusicology in the Anthropocene
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
Abstract In seeking to respond to the environmental challenges of the Anthropocene era, our research team of five scholars, including faculty and advanced graduate students, along with each of their collaborators in their respective research sites, has come together to explore the possibilities of a methodology that we call Field to Media. Field to Media involves using video production to study and amplify ecomusical responses to climate change, pollution, deforestation, and other environmental challenges. This methodology is intended as a pragmatic process that blends participant observation with participatory action research and applied or activist engagement. Specific to this project, our efforts have involved the co-creation of five different music videos to address a range of pressing environment-related matters in USA/Canada, Tanzania, Bangladesh, China, and Haiti. In this article, we consider some of the potential successes and challenges that we have each experienced in the course of producing these music videos.
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) | 0.006 | 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 it