An Interdisciplinary and Intersectoral Action-research Method: Case-Study of Climate Change Adaptation by Cities Using Participatory Web 2.0 Urban Design
Bibliographic record
Abstract
This paper discusses the last segment of a three-year interdisciplinary and intersectoral action research on climate change and urban transformation. The project had, as one of its core missions, the role of imagining urban and architectural adaptations for urban neighbourhoods that would contribute to minimizing the negative impacts of climate change on people's comfort, health and safety. The first part of the paper describes the collaborative design and augmented participation method used in the context of Québec City, Canada. These include the design process conducted to imagine adaptation scenarios, the visual strategies undertaken to make these understandable for the population, and the Web 2.0 crowdsourcing approach forwarded to measure feasibility and social acceptability of the design and visualization strategies. The second part discusses three positive outcomes of the process. First, collaborative design conducted with intersectoral groups of experts constitutes a promising avenue to identify adaptations and evaluate their relevance. Second, crowdsourcing is a powerful tool to inform the general public about climate change including both negative and potential aspects. As well, the crowdsource model allows access to particular knowledge which empowered users to make changes around their homes and neighbourhoods or advocating action from their local government. Crowdsourcing is also an efficient tool to help understand what people know about the potential impact of climate change and how it bears on their comfort, health and safety. Third and finally, the design proposals and the evaluation comments generated by working closely with various stakeholders, along with the public on-line consultation, allow for the induction of pragmatic recommendations that can be used as decision aids by elected officials and civil servants to better prepare their municipalities for climate change.
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How this classification was reachedexpand
Direct model labels (unvalidated)
Per-model category and study-design labels from the labeling rounds. They are machine output, unvalidated, and the disagreement between models ships as data. No study design here is MEDLINE-validated yet.
| Model arm | Categories | Study design | Confidence |
|---|---|---|---|
| gemma | no category Domain: not available · Genre: Empirical About the Canadian research system: no · About a Canadian topic: no | Qualitative | low |
| gpt | no category Domain: not available · Genre: Empirical About the Canadian research system: no · About a Canadian topic: no | Qualitative | high |
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 | 0.024 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.012 | 0.007 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.003 | 0.008 |
| Research integrity | 0.003 | 0.002 |
| 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, unvalidatedLabeled directly by 2 models reading the full record.
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".