An Evaluation of Global Hazard Communication with Ethical Considerations
Bibliographic record
Abstract
INTRODUCTION: Despite the large number of hazards occurring every year, it is often only the most catastrophic and rapidly occurring hazards that are covered in detail by major news outlets. This can result in an under-reporting of smaller or slowly evolving hazards such as drought. Furthermore, the type or country in which the hazard occurs may have a bearing on whether it receives media coverage. The Public Health England (PHE) global weekly hazards bulletin is designed to inform subscribers of hazards occurring in the world in a given week regardless of location or type of natural hazard. This paper will aim to examine whether the bulletin is reporting these events in a way that matches a number of international disaster databases. It will also seek to answer if biases within media outlets reporting of an event is impacting on the types of hazards and events being covered. Through the analysis of data collected, it is hoped to be able to consider the ethical implications of such a bulletin service and provide recommendations on how the service might be improved in the future. METHODS: The study used a year's worth of global hazards bulletins sent by Public Health England. These bulletins aim to communicate hazards in the form of compiled articles from news outlets around the world. Data from these bulletins was collected and analysed by hazard type and the country in which hazards occurred. It was then compared to recognised hazard databases to assess similarities and differences in the hazards being reported via media or through dedicated hazard databases. The recognised hazard databases were those run by the Emergency Events Database (EM-DAT), European Civil Protection and Humanitarian Aid Operations (ECHO) and National Aeronautics and Space Administration (NASA) respectively. RESULTS: The PHE bulletin overall was found to be comparable to other global hazard or disaster databases in terms of hazards included by both country and type of hazard. The PHE bulletin covered a greater number of unique hazard events than the other databases and also covered more types of hazard. It also gave more frequent coverage to the United Kingdom and Canada than the other databases, with other countries appearing less frequently. More generally, the PHE bulletin and the databases it was compared to appear to focus more on hazards either occurring in developed countries or fast-onset ones such as landslides or floods. On the other hand, slow-onset hazards such as drought or those occurring in developing countries appear to be under-reported and are given less importance in both the bulletin and databases. DISCUSSION AND RECOMMENDATIONS: We recommend that the resources compared review their inclusion criteria and assess whether the discrepancies in hazard type and country can be ratified through changes in how hazards are assessed for inclusion. More research should be undertaken to assess whether similar findings arise when comparing databases in other areas within the remit of public health.
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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 | 0.337 | 0.582 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.009 | 0.010 |
| Science and technology studies | 0.005 | 0.017 |
| Scholarly communication | 0.011 | 0.009 |
| Open science | 0.003 | 0.011 |
| Research integrity | 0.004 | 0.004 |
| Insufficient payload (model declined to judge) | 0.008 | 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".