Bibliographic record
Abstract
The analysis and prediction of life expectancy trends hold an important role in many aspects of our society. On a macro level, governments rely on life expectancy data to make decisions about public welfare programs, health care, retirement age, and pension programs. On an individual level, many people take life expectancy data into account when planning their retirements and making decisions about their future. Currently, two main schools of thought dominate the debate on the trajectory of life expectancy trends. The Olshansky School argues that global human life expectancy is reaching its absolute limit and predicts that a worldwide plateau in life expectancy will soon be reached. The Vaupel School, however, believes that such a plateau is nowhere in sight, and that humans possess no biological barrier that will prevent life expectancy from increasing indefinitely. In this commentary, I build upon the evidence generated by the Vaupel School by introducing socioeconomic factors into the debate and I argue that with consistent improvements to medical technology and general prosperity we will not encounter a biological limit to human life expectancy in our lifetimes.
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 | 0.009 | 0.033 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.026 |
| Scholarly communication | 0.006 | 0.010 |
| Open science | 0.004 | 0.003 |
| Research integrity | 0.012 | 0.015 |
| Insufficient payload (model declined to judge) | 0.003 | 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".