Assessing resilience, equity, and sustainability of future visions across two urban scales
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Abstract Cities need to take swift action to deal with the impacts of extreme climate events. The co-production of positive visions offers the potential to not only imagine but also intervene in guiding change toward more desirable urban futures. While participatory visioning continues to be used as a tool for urban planning, there needs to be a way of comparing and evaluating future visions so that they can inform decision-making. Traditional tools for comparison tend to favor quantitative modeling, which is limited in its ability to capture nuances or normative elements of visions. In this paper, we offer a qualitative method to assess the resilience, equity, and sustainability of future urban visions and demonstrate its use by applying it to 11 visions from Phoenix, AZ. The visions were co-produced at two different governance scales: five visions were created at the village (or borough) scale, and six visions were created at the regional (or metropolitan) scale. Our analysis reveals different emphases in the mechanisms present in the visions to advance resilience, sustainability, and equity. In particular, we note that regional future visions align with a green sustainability agenda, whereas village visions focus on social issues and emphasize equity-driven approaches. The visions have implications for future trajectories, and the priorities that manifest at the two scales speak of the political nature of visioning and the need to explore how these processes may interact in complementary, synergistic, or antagonistic ways.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.007 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.005 |
| Science and technology studies | 0.002 | 0.008 |
| Scholarly communication | 0.000 | 0.002 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 it