Bibliographic record
Abstract
On October 28th 2013 Hurricane Sandy hit land on the East Coast of the United States. The deadliest storm to hit the country since 2005 it caused tens of billions of dollars in damage, destroyed thousands of homes, left millions without electric service, and caused 117 deaths in the United States, including 53 in New York, making Sandy the most life costly hurricane to hit the United States mainland since Hurricane Katrina. In all an estimated 186 people were killed across the United States, the Caribbean, and Canada. In the immediate aftermath of the storm not only did the emergency services, state and federal government implement emergency plans of action, including both direct intervention on the ground and massive financial support, so too did a number of charities, community and residents groups across the US. One of the most surprising of these groups was what became known as Occupy Sandy. As noted by the Homeland Security Studies and Analysis Institute: “Within hours of Sandy’s landfall, members from the Occupy Wall Street movement used social media to tap the wider Occupy network for volunteers and aid. Overnight, a volunteer army of young, educated, tech-savvy individuals with time and a desire to help others emerged. In the days, weeks, and months that followed, “Occupy Sandy” became one of the leading humanitarian groups providing relief to survivors across New York City and New Jersey. At its peak, it had grown to an estimated 60,000 volunteers—more than four times the number deployed by the American Red Cross.” What this phenomenon clearly demonstrates is the potential for digital networking to improve response to catastrophic storm events at a community level. Far from being solely a question of material support and logistics, the response to the disaster was one equally definable as digital. Pointing to the possible rethinking of issues around the extreme and localised consequences of climate change and responses to it in purely traditional infrastructural terms, the social media focused organisation of Occupy Sandy potentially offers us a new frame of reference to deal with these, and less catastrophic issues around climate change and our response to it. This paper provides a discussion of the projected impacts of global environmental change in urban environments in the United States, with a particular focus on their impact on existing storm and sanitary water infrastructure. However, it theorizes a new approach to this archaic system of infrastructure that exploits social networking tools and digital technologies to build greater networks for climate change resilience across the United States and, by extension, elsewhere.
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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.007 | 0.002 |
| Scholarly communication | 0.012 | 0.012 |
| Open science | 0.002 | 0.011 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.188 | 0.060 |
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".