“Communities in the middle”: Interactions between drivers of change and place-based characteristics in rural forest-based communities
Bibliographic record
Abstract
Using a socioecological systems perspective, we advance a conceptual approach for characterizing trajectories of change in rural forest-based communities. We call attention to “communities in the middle,” communities positioned within forested regions representing neither unpopulated wilderness nor heavily urbanized or densely populated places on the edge of urban areas. In 2010, these middle places accounted for 27.3% of the continental United States landscape yet less than 5% of the human population. Common shocks, such as the decline of traditional production industries , demographic shifts, new information technologies, climate change , invasive species , and demand for new energy resources, unite these areas. Yet, we observe variation in existing patterns of change across communities, which grows out of interactions between local contexts and larger drivers of change. Focusing on community dynamics, structure, and well-being in transitioning rural forested landscapes, we synthesize insights on three commonly identified development trajectories. We identify interactions among the resource base, connectivity to other places, and social adaptability as critical to these trajectories. Further, we describe vulnerabilities, opportunities, contingencies, diversity, novel recombinations, and mitigation as useful concepts for understanding community pathways within these trajectories. This framework provides a starting point to guide further synthesis, formal meta-analyses, and future interdisciplinary research on change in these important ‘middle’ places.
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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 | 0.003 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.002 | 0.004 |
| Scholarly communication | 0.003 | 0.005 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".