Putting the puzzle together: reducing vulnerability through people-focused planning
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Supporting and integrating vulnerable persons into emergency management has emerged as an increasing priority in emergency management in Canada. Events such as the 2003 European heat wave and Hurricane Katrina have shown that disasters almost always have the harshest affects on the frail elderly, people with disabilities, the disadvantaged and the least able. While it is neither possible--nor the role of emergency management--to eliminate or reduce many factors that make people vulnerable to hazards, it is important that emergency managers work closely with communities to not only identify risks and vulnerabilities, but also to build on the resources and capacities that enable people to effectively prepare for, respond to and recover from threats of all types. To this end, emergency management must take into account the level of capacities and resources that a community has to prepare for emergencies and disasters, in addition to people's vulnerabilities to extreme events. To be successful, people must be viewed not as a part of the problem to be solved or managed during an emergency, but rather a part of the solution to building more resilient and disaster-resistant societies. This requires 'people-focused' planning methodologies that move beyond planning for to planning with all segments of society, including the most vulnerable and marginalized groups that are more readily overlooked. This not only begins to ensure that emergency planning and response capacities can effectively address the diverse needs of all people, but is also an important step to empowering the most vulnerable to prepare themselves for emergencies and other critical events.
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.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it