Tool or Toy? Virtual Globes in Landscape Planning
Bibliographic record
Abstract
Virtual globes, i.e., geobrowsers that integrate multi-scale and temporal data from various sources and are based on a globe metaphor, have developed into serious tools that practitioners and various stakeholders in landscape and community planning have started using. Although these tools originate from Geographic Information Systems (GIS), they have become a different, potentially interactive and public tool set, with their own specific limitations and new opportunities. Expectations regarding their utility as planning and community engagement tools are high, but are tempered by both technical limitations and ethical issues [1,2]. Two grassroots campaigns and a collaborative visioning process, the Kimberley Climate Adaptation Project case study (British Columbia), illustrate and broaden our understanding of the potential benefits and limitations associated with the use of virtual globes in participatory planning initiatives. Based on observations, questionnaires and in-depth interviews with stakeholders and community members using an interactive 3D model of regional climate change vulnerabilities, potential impacts, and possible adaptation and mitigation scenarios in Kimberley, the benefits and limitations of virtual globes as a tool for participatory landscape planning are discussed. The findings suggest that virtual globes can facilitate access to geospatial information, raise awareness, and provide a more representative virtual landscape than static visualizations. However, landscape is not equally representative at all scales, and not all types of users seem to benefit equally from the tool. The risks of misinterpretation can be managed by integrating the application and interpretation of virtual globes into face-to-face planning processes.
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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 | 0.008 | 0.015 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.004 | 0.019 |
| Scholarly communication | 0.011 | 0.024 |
| Open science | 0.002 | 0.008 |
| Research integrity | 0.004 | 0.003 |
| Insufficient payload (model declined to judge) | 0.015 | 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".