Social Security Programs and Retirement around the World: Working Longer
Bibliographic record
Abstract
Frontmatter -- Contents -- Acknowledgments -- Introduction / Coile, Courtney C. / Milligan, Kevin / Wise, David A. -- 1. Older Men's Labor Force Participation in Belgium / Jousten, Alain / Lefebvre, Mathieu -- 2. The Labor Force Participation of Older Men in Canada / Milligan, Kevin / Schirle, Tammy -- 3. From Early Retirement to Staying in the Job: Trend Reversal in the Danish Labor Market / Bingley, Paul / Gupta, Nabanita Datta / Pedersen, Peder J. -- 4. Explaining the Reversal in the Trend of Older Workers' Employment Rates: The Case of France / Blanchet, Didier / Bozio, Antoine / Prost, Corinne / Roger, Muriel -- 5. Old- Age Labor Force Participation in Germany: What Explains the Trend Reversal among Older Men and the Steady Increase among Women? / Börsch-Supan, Axel / Ferrari, Irene -- 6. Employment at Older Ages: Evidence from Italy / Brugiavini, Agar / Pasini, Giacomo / Weber, Guglielmo -- 7. Labor Force Participation of the Elderly in Japan / Oshio, Takashi / Usui, Emiko / Shimizutani, Satoshi -- 8. Why Are People Working Longer in the Netherlands? / Kalwij, Adriaan / Kapteyn, Arie / Vos, Klaas de -- 9. Trends in Labor Force Participation of Older Workers in Spain / García-Gómez, Pilar / Jiménez-Martín, Sergi / Castelló, Judit Vall -- 10. The Recent Rise of Labor Force Participation of Older Workers in Sweden / Laun, Lisa / Palme, Mårten -- 11. Long- Run Trends in the Economic Activity of Older People in the United Kingdom / Banks, James / Emmerson, Carl / Tetlow, Gemma -- 12. Working Longer in the United States: Trends and Explanations / Coile, Courtney C. -- Contributors -- Author Index -- Subject Index
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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.049 | 0.003 |
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".