Reimagining Open Data during Disaster Response: Applying a Feminist Lens to Three Open Data Projects in Post-Earthquake Nepal
Bibliographic record
Abstract
Open Data has become a prominent ideal in humanitarian information work and is increasingly promoted for crisis situations to increase effectiveness, accountability, and empower citizens. However, like all socio-technical systems, open data platforms for disasters make implicit and explicit assumptions about data, data users, disasters, and the context of use. In this paper, we turn to feminist theory to examine three open data projects rolled out in the aftermath of the 2015 earthquake in Nepal. We used the seven principles of Data Feminism introduced by D'Ignazio and Klein to design an evaluative framework for the three projects. We use this framework to highlight and link the socio-political nature of both disasters and open data platforms. In our results, we highlight significant gaps in how these projects made labor (in)visible, engaged with affective aspects of disaster, addressed context, and challenged power. We argue that these gaps are reflective of dominant practices in open data for disasters and serve as opportunities for designers and crisis informatics researchers to reimagine the potential of such projects. We propose four ways of doing so based on feminist principles and values.
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 | 0.023 | 0.024 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.028 | 0.043 |
| Scholarly communication | 0.011 | 0.011 |
| Open science | 0.003 | 0.019 |
| Research integrity | 0.004 | 0.007 |
| Insufficient payload (model declined to judge) | 0.004 | 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".