Situating mortality: Quantifying crisis points and periods of stability
Bibliographic record
Abstract
A wide range of stressors can cause a dramatic and sudden rise in the death rate in populations, typically resulting in what is referred to as crisis mortality. Here we present a method to standardize the assessment of identifying moments of crises. A modification of the mortality Z-score methodology which is combined with time series analysis was used to investigate mortality events over the course of nearly two centuries for two populations: Gibraltar and Malta. A benefit of this method is that it situates the yearly death rate within the prevailing mortality pattern, and by doing so allows the researcher to assess the relative impact of that event against the norm for the period under investigation. A series of threshold values were established to develop levels of mortality to distinguish moments of lower mortality than expected, background mortality, a crisis, and a catastrophe. Our findings suggested that within defined periods, a limited number of events constituted moments of excessive mortality in the range of a crisis or higher. These included epidemics (yellow fever and influenza in Gibraltar only, and cholera) and casualties associated with World War II. Episodes of lower than expected mortality were only detected (although not significant) in the 20th century in Malta, and at the micro level, the harvesting effect appears to have occurred following cholera epidemics in both locations and influenza in Gibraltar. The analysis demonstrates clearly that the impact of epidemics can be highly variable across time and populations.
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.
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 | 0.001 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".