Classifying climate vulnerability and inequalities in India, Mexico, and Nigeria: A latent class analysis approach
Bibliographic record
Abstract
Abstract The climate crisis exacerbates social, economic, and health disparities. This study employs innovative methods to identify distinct groups affected by recent climate events. A mobile phone-based survey was conducted in April 2022 with individuals residing in multiple climate-affected states across three countries: India ( n = 1020), Mexico ( n = 1020), and Nigeria ( n = 1021). Latent class analysis and classification and regression tree analysis were used to identify the groups most exposed to climate events, the effects and responses taken, and then to identify the characteristics associated with group membership. Effects included housing damage or lost work, while responses included actions such as borrowing money or dropping out of school. Findings revealed four distinct groups: Group 1 reported low exposure, no effects, or responses (49% of respondents in India, 43% in Mexico, and 27% in Nigeria); Group 2 experienced multiple hazards with moderate effects and some responses; Group 3 was characterized by drought exposure with more effects and responses taken; Group 4 was affected by heavy flooding and rainfall with varied effects. Notably, India had the largest proportion of respondents in Group 3 (17%), in Mexico over a quarter (29%) were in group 4, while over half of Nigerian respondents were in Group 2 (52%). Characteristics associated with membership in each group varied by country. Overall, men from rural areas with lower incomes and reliant on agriculture experienced the highest levels of exposure and vulnerability, while urban women from higher-income households were the least affected. This study underscores the importance of considering the intersectionality of risk and vulnerability when formulating policies and programs to address the impacts of climate change. Results emphasize the need for multi-sectoral policies that target the needs of different groups, to reduce inequalities and tailor to the context-specific needs of the most vulnerable people and households.
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| 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".