Making Maps That Matter: Situating GIS within Community Conversations about Changing Landscapes
Bibliographic record
Abstract
Geospatial analysis and mapping has tremendous potential to inform community-scale deliberations about land use and growth management, but that potential is rarely realized. This article introduces an iterative, participatory research approach to generating maps about landscape change and development trends rooted in local experiences of place and therefore well positioned to contribute to civic dialogue and action. The research process involved collaboration with community partners; ethnographic interviews to identify salient local issues and perspectives; geospatial analysis, mapping, and visualizations of development trends; focus groups to refine information and imagery for local audiences; and deliberative meetings designed to encourage public discussion. Through a case study from a rapidly growing Southern Appalachian county, we show how this process aided the development of maps and visualizations that were relevant and accessible to local stakeholders, made visible local concerns about landscape change, and increased stakeholders' awareness of landscape-scale processes. We argue that this interdisciplinary approach can help to bridge between critical and analytic GIS traditions, provide a mechanism for integrating research agendas with local policy deliberations, and help foster successful civic dialogues and collective action in communities with histories of contentious debate about land-use planning.
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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 | 0.006 | 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 | 0.006 | 0.000 |
| Scholarly communication | 0.001 | 0.004 |
| Open science | 0.001 | 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 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".