Earthquake‐disaster preparedness: the case of Accra
Bibliographic record
Abstract
Purpose – Accra, the capital of Ghana is far away from major earthquake zones of the world, but has a history of destructive earthquakes. However, its seismic risk does not attract the requisite attention. The purpose of this paper is to give an overview of Accra's seismic risk, discuss challenges faced and risk-reduction initiatives, and then to propose specific strategies that are necessary to reduce this risk.\nDesign/methodology/approach – The approach taken is to: give an overview of Accra's profile and seismicity; discuss disaster management structures in place and the challenges faced; discuss seismic risk-reduction programs; discuss the risk-reduction strategies of two cities in other developing countries, with the view of identifying specific strategies that would be helpful to Accra; and conclude with specific risk-reduction action measures that are important for Accra.\nFindings – A number of specific recommendations to reduce Accra's seismic risk are made at the end of the paper. Among these, the need to set up a national organization with the sole mandate of championing seismic risk reduction is identified as a critical step needed. Without this, and others, the paper contends that Accra would not experience any significant reduction of its seismic risk.\nSocial implications – The paper presents a viewpoint of important action steps that need to be taken to reduce Accra's seismic risk. The points raised in the paper are considered as important first steps necessary for any form of sustainable disaster risk reduction. The paper would thus be of interest to any person or organization interested in helping reduce Accra's seismic risk.\nOriginality/value – This is the first paper to put Accra's seismic risk in a global context, and then propose action steps that are necessary to help reduce this risk.
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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 | 0.001 | 0.002 |
| Science and technology studies | 0.011 | 0.004 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.004 | 0.004 |
| Insufficient payload (model declined to judge) | 0.009 | 0.001 |
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".