Spaces, places and possibilities: A participatory approach for developing and using integrated models for community planning
Bibliographic record
Abstract
Integrated models can support community planning efforts because they have the ability to elucidate social, economic, and environmental relationships and outcomes associated with different local development plans and strategies. However, deciding what to include in an integrated model presents a significant challenge, as including all aspects of a community and local environment is unfeasible, whereas including too few aspects leads to a non-representative model. This research aimed to address this challenge by employing an iterative, participatory process in an integrated modelling effort. Conducted in Squamish (BC, Canada), the research involved developing a community systems model and scenarios (i.e., different community development patterns), modelling the scenarios, evaluating the model through a community focus group, and refining the model and scenarios based on the feedback. Much of the work developing the initial systems model and scenarios was done a previous research phase, and it involved assembling local government and community stakeholder focus groups to discuss issues and possible futures for Squamish. Analysis of the focus group data informed the design of a community systems model and local development scenarios, which were subsequently applied in an integrated modelling exercise. Modelling primarily used ArcGIS and R, and explored a variety of factors including access to amenities, education, walkability, parks/trails, food and farm systems, public transit, housing affordability, threats to critical habitat, etc. Another local government and community stakeholder focus group was held to solicit feedback on the model and scenarios, which were then refined based on the feedback. The research found the participatory approach to beneficial for creating community planning tools with high relevance to local contexts and needs. The model developed in this work has great potential for supporting community planning because it effectively identifies the co-benefits and trade-offs of different development strategies. It is important to develop these types of community planning tools through iterative processes, where they are refined through multiple stages of feedback by a variety of stakeholders, to better capture the local concerns and realities of a place.
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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 | 0.077 | 0.056 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.001 | 0.003 |
| Bibliometrics | 0.005 | 0.004 |
| Science and technology studies | 0.012 | 0.013 |
| Scholarly communication | 0.011 | 0.010 |
| Open science | 0.006 | 0.021 |
| Research integrity | 0.003 | 0.006 |
| Insufficient payload (model declined to judge) | 0.008 | 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".