GIS-based multicriteria evaluation for earthquake response: a case study of expert opinion in Vancouver, Canada
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
Abstract GIS-based multicriteria evaluation (MCE) provides a framework for analysing complex decision problems by quantifying variables of interest to score potential locations according to their suitability. In the context of earthquake preparedness and post-disaster response, MCE has relied mainly on uninformed or non-expert stakeholders to identify high-risk zones, prioritise areas for response, or highlight vulnerable populations. In this study, we compare uninformed, informed non-expert, and expert stakeholders’ responses in MCE modelling for earthquake response planning in Vancouver, Canada. Using medium- to low-complexity MCE models, we highlight similarities and differences in the importance of infrastructural and socioeconomic variables, emergency services, and liquefaction potential between a non-weighted MCE, a medium-complexity informed non-expert MCE, and a low-complexity MCE informed by 35 local earthquake planning and response experts from governmental and non-governmental organisations. Differences in the observed results underscore the importance of accessible, expert-informed approaches for prioritising locations for earthquake response planning and for the efficient and geographically precise allocation of resources.
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 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| 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