Archived Newspaper Reports as a Complementary Source of Epidemiological Data for Research into Climate Change Adaptation
Bibliographic record
Abstract
The impact of weather extremes on population health can be studied using descriptive information about pre-and post-event circumstances.Yet, descriptive data are not typically recorded in administrative databases used in quantitative health research by epidemiologists.This chapter introduces, describes and validates a method for using newspaper reports to complement traditional epidemiological data sources for research on climate change impacts.As per the innovative methods and systems-based approaches advanced by Anthony (Tony) J. McMichael in his research across a broad array of epidemiological enquiry, our method development focuses on four areas by combining qualitative and quantitative methods: first, selecting and extracting information regarding extreme weather-related events and extreme weather-related disasters, and linking them to the appropriate newspapers; second, creating a content analysis framework (CAF) and extracting factual health data (i.e.manifest content) and its implied meaning (i.e.latent content) from newspaper reports; third, verifying the factual health-related data found in newspaper reports; and, fourth, corroborating the number of deaths cited in mortality data.Our research supports the use of newspaper reports related to extreme weather as a complementary source of contextual epidemiological data when assessing climate-related health impacts to support more traditional epidemiological research in health and social policy.
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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 | 0.007 | 0.014 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.009 | 0.013 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.010 | 0.007 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.027 | 0.014 |
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".