Soundhikes and Oral Histories from Appalachian Protected Lands: Implications for Equitability and Access
Bibliographic record
Abstract
In the 1960s and 1970s, Canadian music educator R. Murray Schafer developed the field of soundscape studies, later introducing the “soundwalk”: an empirical method for identifying a soundscape and its components through the activities of walking and close listening. Human activity in a soundscape, or a soundwalk, is known as anthropophony: a category of sounds produced by humans, including language, vocalizations, and musics. In this individual oral presentation, the primary researcher will share her experiences in creating a framework for her Master’s in Appalachian Studies Applied Project at East Tennessee State University. The soundwalk method will be adapted into a series of recorded “soundhikes.” During these hikes, the primary researcher and her participants will engage in a series of interviews in areas designated as state parks and national forests in the Upstate of South Carolina. These oral histories (with English-language translations, when applicable) will be submitted to the Archives of Appalachia, and will be used in the primary researcher’s ongoing research on Appalachian representation in the region’s protected spaces. As these protected spaces have reached nearly one hundred years of existence as “state parks” or “national forests”, the demographics of the people visiting them have changed drastically. The lenses of Critical Race Theory and Feminist Disability Theory will be used to explore these new demographic realties, and to advocate for resources benefitting those who have self-reported any of the following while attempting to enjoy Appalachian nature spaces: barriers to access, feelings of fear, or experiences of rejection. The ultimate goal of the oral history soundhike project, influenced by the principles of Public Sphere Theory, is to feature present-day voices which have not been historically included in Appalachian “nature narratives,” leading to increased representation in the field of Appalachian Studies. Though data collection has not yet begun, it is the hope of the primary researcher that this information will have a pragmatic application. Applicable portions of this project will be submitted to agencies, located in South Carolina’s Upstate region, which have indicated an interest in attracting more diverse stakeholders. The finished project will advocate for more diverse language and cultural resources and programming in protected nature spaces.
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How this classification was reachedexpand
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 | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".