Towards the Development of an Anti-Colonial Critique of Climate and Disaster Risk Models
Bibliographic record
Abstract
Technology mediated environmental data increasingly shapes how we understand the world, including pressing ecological issues such as disasters and climate change.However, like all data, environmental data is limited and partial.It is necessary to attend to the decisions and practices that create and use this data to understand their limits and advocate for alternatives.In this paper, we draw on postcolonial, decolonial, and anti-colonial theory and a case study of a multi-hazard disaster and climate risk assessment project conducted in Nepal to examine a potential limit of contemporary environmental data practices -the potential to extend or reinforce colonial knowledge systems and extractive relationship to land.Through our analysis, we draw attention to how environmental data practices, such as disaster risk assessment, may contribute to ongoing colonial relationships by privileging technocratic Eurocentric knowledge, conflating disaster effects with economic loss, ignoring ecological impact, and overlooking historical and ongoing power hierarchies.We build on our findings to think through opportunities to reimagine disaster and climate risk beyond probabilistic quantitative models.To do so, we propose four tactics towards an anti-colonial science of risk, as well as argue for a more thorough analysis that attends to situated practices of creating and using environmental data.
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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 | 0.035 | 0.043 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.006 | 0.091 |
| Scholarly communication | 0.013 | 0.019 |
| Open science | 0.003 | 0.009 |
| Research integrity | 0.007 | 0.022 |
| 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".