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Paper short abstract

Systemic corruption and uneven data ecosystems undermine the justice and equity of Early Warning Systems (EWS); thereby producing exclusionary multi-hazard prone communities. The paper presents gender-transformative indicators through intersectional data feminist lenses for inclusive EWS.

Paper long abstract

Early Warning Systems (EWS) gain effectiveness through the data governance systems that inform them. This paper examines how misaligned transparency regimes, compounded by systemic corruption, produce data injustices that weaken disaster risk reduction (DRR) and anticipatory finance for marginalized communities. Data feminism assists in revealing the nexus of corruption and gender-unaware early warning systems as impediments to inclusive Disaster Risk Reduction. The paper establishes that corruption operates as a data‑quality parameter in procurement cascades, opaque intermediaries as well as misclassification of finance. The paper examines data feminist theories such as data ecofeminism critically to analyse the downstream effects on EWS triggers, false positives, and inequitable resource allocation. The paper displays how corruption leads to distortion in Disaster Risk Reduction (DRR) outcomes for marginalized communities. The paper presents gender-transformative indicators designed to make anticipatory action auditable, equitable, and responsive to marginalized sections of communities.

The paper therefore advocates for integrating diverse local datasets into people‑centred EWS for epistemic and practical data justice. The paper achieves this by critical examination of data feminism and data cyberfeminism as gendered theories for data justice in EWS.