Seeking refuge? The potential of urban climate shelters to address intersecting vulnerabilities
Bibliographic record
Abstract
Climate shelters are critical urban infrastructures to support adaptation to extreme weather. They offer spaces – e.g., parks, libraries, and civic centers – where residents can take refuge during episodes of extreme temperatures. With over 200 public spaces designated as “Climate Shelters”, Barcelona (Spain) serves as an emblematic example of whether these emerging spaces are meeting the needs, expectations, and everyday experiences of the most vulnerable residents. By applying an intersectional climate justice perspective and mixed-method approaches rooted in a survey of a particularly climate-exposed working-class neighborhood (La Prosperitat), we found that the intersecting vulnerabilities of marginalized populations remain poorly addressed, largely due to differences in access to coping mechanisms that overlap with intersecting social positions, exacerbating vulnerability to climate risks. We also found that housing inadequacy and energy poverty experienced by low-income residents and those originally from Global South countries made them the most affected and least able to cope with extreme temperatures. Women were also more affected by climate impacts and more concerned about current and future risks. We argue that unequal lived experiences of thermal (dis)comforts inform heat and cold inequalities, which, in turn, are attributed to intersecting social positions and structural vulnerabilities. These uneven lived experiences shape – and are reshaped by – limited adaptive capacity, culturally inappropriate approaches, and insufficiently inclusive public spaces, thus complicating an equity-driven provision of refuge infrastructures. Results call for developing refuge infrastructures that address the intersecting social and climate needs of residents who need them the most.
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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 | 0.004 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.005 | 0.006 |
| Scholarly communication | 0.007 | 0.006 |
| Open science | 0.002 | 0.018 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.011 | 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".