The Localisation of Vulnerabilities: The construction of risks and hazards in the coverage of earthquakes in the Seattle Times and the Vancouver Sun
Bibliographic record
Abstract
Despite an increasing willingness among academics politicians, policymakers, non-governmental organisations [NGOs], businesses, practitioners and citizens to confront the risks posed by disasters, many attempts at implementing measures of disaster risk reduction [DRR] have been unsuccessful. Much has been written about disaster risk reduction, but none of the literature has effectively analysed the necessary conditions for disaster risk reduction to be successful in an at-risk community.\n\nVan Belle argues that the localisation of distant disasters – the practice where a news outlet covers an external event from their own locality’s point of view and interprets that event in terms of how it relates to them – is essential for DRR to become on the public agenda and create the opportunity for DRR policy to be successfully pursued (2012). This thesis adds to the understanding of the news coverage of non-local disaster events by analysing how disasters are localised by the news media to relate to the hazards faced by their communities. It was found that overseas disaster events must not only be localised, but also communalised through direct comparisons between communities in the news coverage for DRR to become on the public agenda in at-risk communities.\n\n229 newspaper articles were analysed through a structured qualitative content analysis. Localisation occurred in the Seattle Times and the Vancouver Sun following six overseas earthquakes. It was found that the nature of the coverage changed according to the stricken country’s level of development, where more direct comparisons were made between communities after the earthquakes in Japan, Chile and in Turkey in some instances. However, the coverage of the earthquakes in Turkey, Pakistan and Haiti led to the establishment of a paternalistic victim-saviour type relationship between communities in the newspapers.\n\nThese findings have significant implications for the implementation of disaster risk reduction in at-risk communities and for the understanding of the production of news. Additionally, the theoretical practice of localisation was developed and operationalised. This led to the formulation of five typologies of localisation that illustrated the nature of the coverage of the earthquakes in the two leading broadsheet newspapers in the Pacific Northwest. Significantly, the thesis suggests that the nature of the localisation may depend on their level of identification with the stricken community.
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How this classification was reachedexpand
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 | 0.003 | 0.000 |
| 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 | 0.002 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".