Under the Weather:Reimagining Mobility in the Climate Crisis
Bibliographic record
Abstract
Humans and human mobility, including driving and flying, are entangled with the climate emergency. Fossil-fuelled mobility worsens severe weather, and in turn, severe weather disrupts human mobility. A shift to zero-emission vehicles is critical but insufficient to repair the damage or prepare communities for the coming disruptions severe weather will bring. In Under the Weather Stephanie Sodero explores the intersection between human mobility and severe weather. Anchored in two Atlantic Canadian hurricane case studies, Hurricane Juan in Mi'kma'ki/Nova Scotia in 2003 and Hurricane Igor in Ktaqmkuk/Newfoundland in 2010, the book contributes to contemporary cultural and policy discussions by offering five practical recommendations - revolutionize mobility, prioritize vital mobility of medical goods and services, embrace ecological mobilities, rebrand redundancy, and think flexibly - for how mobility can be reimagined to work with, rather than against, the climate in ways that also benefit the health, education, and economy of local communities. This ecological approach to mobilities sheds light on extreme mobility dependency and the impact of mobility disruptions on the ground in Canadian communities. Focusing on the entangled relationship between human mobility and the climate, Under the Weather examines how communities can transform their relationship with mobility to enable greater resilience.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.004 | 0.006 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 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, 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".