Building resilient communities: A facilitated discussion
Bibliographic record
Abstract
The Building Resilient Communities Workshop was hosted and organized by the Justice Institute of British Columbia, with the support of Emergency Management British Columbia and the Canadian Safety and Security Program, Defence Research and Development Canada, Centre for Security Science. Thirty-four participants from multiple levels of government, senior practitioners, policy makers, academia, community members, and a variety of agencies disseminated knowledge and developed concrete strategies and priority actions areas for supporting ongoing and emerging initiatives in community and disaster resilience planning. Identified strategies included development of an integrated national strategy and finding ongoing sustainability funding; increasing community engagement through information sharing, giving context-specific examples of anticipated outcomes, and demonstrating return on investment; as well as the need to engage and support local champions and embedding disaster resilience within other processes. A key message was that communities should be encouraged to use ANY tool or process, rather than struggling to find the perfect tool. Any engagement with disaster resilience planning increases community resilience.
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 | 0.046 | 0.047 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.039 | 0.015 |
| Scholarly communication | 0.011 | 0.014 |
| Open science | 0.005 | 0.040 |
| Research integrity | 0.009 | 0.011 |
| Insufficient payload (model declined to judge) | 0.016 | 0.003 |
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".