Bibliographic record
Abstract
Contents: Will Population Ageing Decrease Productivity? Symposium on Population Ageing and Economic Productivity, December 2-4, 2004, Vienna Institute of Demography; Alexia Prskawetz: Background and Summary of Discussion; Vegard Skirbekk: Productivity Decreases with Age; Thomas Lindh: Productivity is a System Property and Need Not Decrease with the Age of Workforce; M. N. Bhrolcháin and L. Toulemon: Does Postponement Explain the Trend to Later Childbearing in France?; C. Bühler and D. Philipov: Social Capital Related to Fertility: Theoretical Foundations and Empirical Evidence for Bulgaria; Tomás Sobotka, Maria Winkler-Dworak, Maria Rita Testa, Wolfgang Lutz, Dimiter Philipov, Henriette Engelhardt, and Richard Gisser: Monthly Estimates of the Quantum of Fertility: Towards a Fertility Monitoring System in Austria; A. Prskawetz and B. Zagaglia: Second Births in Austria; Martin Spielauer: Concentration of Reproduction in Austria: General Trends and Differentials by Educational Attainment and Urban-Rural Setting; F. Trovato: Narrowing Sec Differential in Life Expectancy in Canada and Austria: Comparative analysis; R. Kronberger: Welche Bedeutung hat eine alternde Bevölkerung für das österreichische Steueraufkommen?; W. Lutz and S. Scherbov: Will Population Ageing Necessarily Lead to an Increase in the Number of Persons with Disabilities?; Recent Demographic Trends in Austria (R. Gisser); Fertility in Austria: An Overview (T. Sobotka)
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 | 0.004 | 0.001 |
| 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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.004 |
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; both teacher heads 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".