Bibliographic record
Abstract
This paper delves into the intrinsic connection between economic growth and life expectancy, illustrating how periods of GDP expansion contribute significantly to global and national increases in average life spans. Over the past two centuries, global life expectancy has experienced a remarkable surge, rising from an average of around 30 years to over 70 years in contemporary times, a trend closely aligned with historical phases of economic growth. Beginning with the industrial revolutions of the mid-19th century and extending to the Digital Revolution of the 20th century, economic prosperity has played a pivotal role in driving improvements in health outcomes. Investments in healthcare, education, infrastructure, and social welfare programs have been facilitated by sustained GDP growth, resulting in enhanced life expectancy and well-being for populations worldwide. Through empirical data and historical analysis, this study underscores the critical nexus between economic policies, technological advancements, and public health initiatives in shaping population longevity. The findings highlight the importance of continued economic development and strategic investments in addressing global health challenges and improving life expectancy across diverse socio-economic contexts.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".