Considering Convergence, Coordination, and Social Capital in Disasters
Bibliographic record
Abstract
Following the 2001 World Trade Center disaster, New York City experienced high levels of individual and organizational convergence: volunteers and groups wanting to assist in the response. Since that time, several initiatives across the U.S. have developed to encourage volunteer disaster response integration. Before 9/11, other formal and informal volunteer organizations had worked toward similar goals, and community-based disaster mitigation was \ntouted as a valuable approach in both Canada and the U.S. Drawing upon examples from research conducted after the 2001 World Trade Center disaster response in New York City as well as research on community based mitigation and response programs, this presentation outlines important considerations when planning for volunteer and community wide participation in disaster reduction and response strategies. Findings point to the value of incorporating community-based groups in disaster related issues and decision making, as well as recognizing the social capital, resources, and expertise these groups bring to the table. This presentation also stresses the need to balance the real considerations and challenges that accompany public integration. Establishing and maintaining partnerships, incorporating groups not traditionally involved in disaster response or mitigation decision-making, setting boundaries, credentialing, familiarizing volunteers with existing response systems, and leveraging initiatives to maximize mitigation opportunities are some of the issues discussed.
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 | 0.008 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.010 | 0.018 |
| Scholarly communication | 0.011 | 0.010 |
| Open science | 0.001 | 0.014 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".