Bibliographic record
Abstract
Increasing longevity, driven by advances in human health and broad improvements to quality of life, is one of the greatest achievements of our time. The 2015 Revision of World Population Prospects states that life expectancy at birth is projected to rise from 70 years in 2010–2015, to 77 years in 2045–2050, and to 83 years in 2095–2100 (UN 2015: 6). This global triumph, however, creates unique challenges concerning both the economic and social structure of society, since the global population is ageing at a rate “without parallel in the history of humanity” (UN 2001: xxviii). Approximately one-quarter (26 per cent) of the world’s people are under 15 years of age, 62 per cent are aged 15–59 years, and 12 per cent are aged 60 or over (UN 2015: 1). The population aged 60 or over, which currently stands at 901 million, is the fastest growing—at a rate of 3.26 per cent per year. By 2050, all major areas of the world, except Africa, will have nearly a quarter or more of their populations aged 60 or over. Where future population dynamics are largely dependent on fertility rates, many countries are experiencing below-replacement fertility, and global fertility is projected to fall throughout the rest of this century. As such, the age structure of the global population is shifting from the traditional population pyramid towards the so-called “population dome” (The Economist 2014). These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.
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 | 0.002 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.007 | 0.005 |
| Open science | 0.003 | 0.005 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.461 | 0.280 |
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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".