Bibliographic record
Abstract
Background: Women outlive men in all but the poorest and most unequal of countries, despite recent increases in male life expectancy that have exceeded those amongst women. This study identifies sources of disparity in longevity between sexes. Methods: Canadian data on age and cause of all deaths recorded in 1999 are grouped and analyzed to identify sex differences in mortality. Results: The overall ratio of male to female deaths (1.09 to 1) varies across ages, from a maximum of 2.6 male for every 1 female death between ages 15 and 29 years, to a minimum of 0.80 to 1 amongst those over 74 years old. The source of greatest disadvantage for men under age 45 years is behaviours attributable to gender. Accidents, injuries, and suicides account for the majority of the male mortality excess in this group, and for more male than female deaths amongst all but the most elderly. Before age 60 years, risk-taking behaviour claims more male lives than does circulatory disease. Conclusions: Data presented show the significance of gender as a determinant of longevity, and suggest the value of interventions to ameliorate this effect.
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 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 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".