Bibliographic record
Abstract
Purpose – The purpose of this paper is to analyze a few recent earthquakes, gain insights into the role of social resilience in the severity of disaster impact and offer plausible approaches to mitigate future disaster impact. Managing and alleviating social and psychological harm among people, in the face of recurring disasters in the world, is very important. Design/methodology/approach – An approach of event comparison has been adopted in this paper. Three recent earthquake events, the 2012 event in Haida Gwaii, Canada, the 2010 event in Christchurch, New Zealand, and the 2011 Japan earthquake and tsunami have been examined through the lens of social resilience of affected population. Findings – Japanese people illustrated patience, tolerance and consideration for other impacted people, proving that it is an effective and efficient approach to dealing with a disaster. New Zealand’s resilience can be attributed to having a governance that is well aware of the hazards in the country. In Canada, however, as of 2001, there are barely any government-funded programs geared toward seismic risks research. Although economically, politically and technologically similar countries can easily learn from this review on resilience, it is important to recognize that there are limitations. Originality/value – The research provides a unique point of view into three different cases of earthquake occurred recently in developed economies. The analysis presented in the paper focuses on social resilience, governance and people’s reaction to the disaster which is vital for disaster risk reduction strategies and programs development as well as implementation.
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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.003 | 0.006 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".