Evaluating presentation formats of local climate change in community planning with regard to process and outcomes
Bibliographic record
Abstract
This study synthesizes two evaluations of a local climate change planning process in a rural town in British Columbia (Canada), which was supported through landscape visualizations. First, the impact of the visualizations, based on scientific environmental modeling and presented in three different presentation formats, verbal/visual presentation, posters and a virtual globe, was evaluated with regard to immediate impacts during the process. Second, the long-term impacts on decision-making and actual outcomes were evaluated in a retrospective evaluation 22 months after the end of the initial planning process. Two results are highlighted: according to the quantitative pre-/post-questionnaires, the visualizations contributed to increased awareness and understanding. Most importantly, the retrospective evaluation indicated that the process informed policy, operational and built changes in Kimberley, in which the landscape visualizations played a role. The post interviews with key decision-makers showed that they remembered most of the visualizations and some decision-makers were further using them, particularly the posters. The virtual globe seemed to be not a “sustainable” display format suitable for formal decision-making processes such as council meetings though. That may change with the further mainstreaming of visualization technologies or mobile devices. Until then, we recommend using display formats that can be re-used following a specific planning event such as an Open House, to ensure on-going support for effective decision-making over the longer-term.
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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 | 0.031 | 0.121 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.006 | 0.003 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.009 | 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".