Vulnerability and its discontents: the past, present, and future of climate change vulnerability research
Bibliographic record
Abstract
The concept of vulnerability is well established in the climate change literature, underpinning significant research effort. The ability of vulnerability research to capture the complexities of climate-society dynamics has been increasingly questioned, however. In this paper, we identify, characterize, and evaluate concerns over the use of vulnerability approaches in the climate change field based on a review of peer-reviewed articles published since 1990 (n = 587). Seven concerns are identified: neglect of social drivers, promotion of a static understanding of human-environment interactions, vagueness about the concept of vulnerability, neglect of cross-scale interactions, passive and negative framing, limited influence on decision-making, and limited collaboration across disciplines. Examining each concern against trends in the literature, we find some of these concerns weakly justified, but others pose valid challenges to vulnerability research. Efforts to revitalize vulnerability research are needed, with priority areas including developing the next generation of empirical studies, catalyzing collaboration across disciplines to leverage and build on the strengths of divergent intellectual traditions involved in vulnerability research, and linking research to the practical realities of decision-making.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.086 | 0.119 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.008 | 0.010 |
| Science and technology studies | 0.005 | 0.032 |
| Scholarly communication | 0.019 | 0.028 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.004 | 0.007 |
| Insufficient payload (model declined to judge) | 0.002 | 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 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".