Envisioning Local Futures: The Evolution of Community Visioning as a Tool for Managing Change
Bibliographic record
Abstract
Citizens' right to participate in decision-making and the planning of their future is a key tenet of western democratic thought. Community visioning is one process for community engagement that has been used to affirm the principles of democracy and address issues facing society today. Paula A. Ding interviews Steven C. Ames, a leading expert and prolific writer on visioning. Steven Ames is a consulting long-range planner and futurist, author of A Guide to Community Visioning (American Planning Association. 1993, 1998) and developer of the Oregon Model of community visioning. He has worked with numerous communities in North America and Oceania, including Maroochy 2025 and Blue Mountains Our Future in Australia, Flagstaff 2020 and Hillsboro 2020 in the US., Future Path Canterbury in New Zealand, and Alberta 2020 in Canada. In their conversation, they touch on several matters including the visioning process, common pitfalls, elements of successful visioning, and implementation. Ding's contention is that community visioning is an innovative tool that can compliment traditional planning practice and offer communities high-level input into the decision-making process on a range of issues and concerns. By facilitating deliberative engagement that is publicly advocated at both an organisational and individual level, community visioning can provide a practical vehicle for driving community well-being and the democratic principles advocated in theory.
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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 | 0.029 | 0.021 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.007 | 0.005 |
| Science and technology studies | 0.010 | 0.071 |
| Scholarly communication | 0.023 | 0.028 |
| Open science | 0.003 | 0.016 |
| Research integrity | 0.006 | 0.007 |
| 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".