GIS Technology in Natural Resource Management: Process as a Tool of Change
Bibliographic record
Abstract
Natural resource management in the United States has experienced dramatic change since landmark legislation in the 1960s and 1970s ultimately brought about high-visibility policy decisions on the public lands of the Pacific Northwest in the 1990s. The socio-political trajectory of that change has moved from institutionally imposed, agency-based decisions toward greater public involvement, increasingly calling upon new technologies to analyse data and communicate scientific findings. An investigation of the use of GIS technology in public involvement in the Coastal Landscape Analysis and Modeling Study in western Oregon finds that use of this technology plays a potentially transformative role that can encourage further movement along this social change–based trajectory but can also constrain it. Use of the technology can constrain change by increasing awareness of uncertainty and by supporting the development of privileged knowledge as held by GIS map-makers, typically scientists. It can encourage change by supporting broader kinds of inquiry and data input, reducing the effects of epistemological differences between scientists and non-scientists, and enhancing the story-making capacity and imagination of all stakeholders. In these respects, the use of GIS technology carries some potential to shift power relationships among scientists and non-scientists participating in the creation of new knowledge. Lasting change along these lines takes time, requiring the building of mutual trust and suggesting that the process of using GIS to analyse and describe landscapes can itself become a tool of change.
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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 | 0.015 | 0.016 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.004 | 0.006 |
| Science and technology studies | 0.005 | 0.039 |
| Scholarly communication | 0.017 | 0.022 |
| Open science | 0.002 | 0.009 |
| Research integrity | 0.004 | 0.004 |
| Insufficient payload (model declined to judge) | 0.005 | 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 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".