Appalachian Social Cohesion: Interviewing, Engagement and Participant Observation in Rural Appalachian Media Markets
Bibliographic record
Abstract
Appalachia is a region in the eastern United States which stretches from northern Mississippi to New York and into Canada. According to the Appalachian Regional Commission, it includes 13 states and more than 206,000 square miles (Appalachian Regional Commission, no date). In Appalachian Studies, the concept of radical resourcefulness is both descriptive and prescriptive: it reflects the radically impoverished resources the people of Appalachia need work with and yet prescribes a form of resourcefulness that allows people to create something from nothing (Carey 2020). Prior research reflects that when radical resourcefulness can be employed, individuals can weather seemingly insurmountable conditions and work in a pro-social manner (Perreault & Richards, 2022; Richards & Perreault, 2021). When scholars research communities in which they are based, they are granted insights which outsiders might not have, but at the same time they must use grounded thought and reflexivity to address research bias. This chapter additionally provides insight in working with smaller, historically impoverished and marginalized populations and rethinking research saturation through multi-step research designs. This knowledge is useful to both journalists and communications professionals who regularly interact with or pitch to Appalachian journalists and news organizations.
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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 | 0.004 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.015 | 0.007 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".