Datafying Disaster: Institutional Framings of Data Production Following Superstorm Sandy
Bibliographic record
Abstract
In the wake of disasters, communities organize to produce spatial data capturing knowledge about the disaster and to fill gaps left by formal emergency responders. The ways in which communities affect overall response efforts can produce inequalities, disempowerment, or further marginalization. Increasingly, this organizing and knowledge production occurs through digital technologies and, recently, digital humanitarianism has become an important suite of such technologies. Digital humanitarianism includes technologies like the crowd-sourced crisis mapping platform Ushahidi and the community of volunteers Humanitarian OpenStreetMap Team, which focuses on the amateur-generated global base map OpenStreetMap. Digital humanitarianism is shifting how needs and knowledges are captured and represented as data following disasters. These transformations raise important questions for geographers interested in the sociopolitical and institutional processes that frame data production and representation. In this article, I contribute to geographers' efforts to understand the institutional and community-based politics that frame the types of data that are produced in disaster contexts by drawing on an ethnographic project that took place in both Washington, DC, and New York City after Superstorm Sandy in 2012. I show that digital humanitarians produced data in the Rockaway Peninsula of New York in response to perceived gaps on the part of formal emergency responders. In so doing, they represented needs, individuals, and communities in ways that local community advocacy organizations found problematic. These findings shed light on the politics and struggles around why particular data sets were produced and the motives behind capturing particular disaster-related needs and knowledge as 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 | 0.038 | 0.058 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.006 | 0.007 |
| Science and technology studies | 0.020 | 0.055 |
| Scholarly communication | 0.020 | 0.021 |
| Open science | 0.003 | 0.022 |
| Research integrity | 0.003 | 0.006 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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".