Riskscapes and the socio-spatial challenges of climate change
Bibliographic record
Abstract
Abstract Anthropogenic climate change is increasing the frequency and severity of the physical threats to human and planetary wellbeing. However, climate change risks, and their interaction with other “riskscapes”, remain understudied. Riskscapes encompass different viewpoints on the threat of loss across space, time, individuals and collectives. This Special Issue of the Cambridge Journal of Regions, Economy, and Society enhances our understanding of the multifaceted and interlocking dimensions of climate change and riskscapes. It brings together rigorous and critical international scholarship across diverse realms on inquiry under two, interlinked, themes: (i) governance and institutional responses and (ii) vulnerabilities and inequalities. The contributors offer a forceful reminder that when considering climate change, social justice principles cannot be appended after the fact. Climate change adaptation and mitigation pose complex and interdependent social and ethical dilemmas that will need to be explicitly confronted in any activation of “Green New Deal” strategies currently being developed internationally. Such critical insights about the layered, unequal and institutional dimensions of risks are of paramount import when considering other riskscapes pertaining to conflict and war, displaced people and pandemics like the 2019–2020 global COVID-19 pandemic.
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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 | 0.006 | 0.022 |
| Meta-epidemiology (narrow) | 0.004 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.006 | 0.007 |
| Scholarly communication | 0.012 | 0.007 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.015 | 0.027 |
| Insufficient payload (model declined to judge) | 0.009 | 0.005 |
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".