Pensions, policy drift and old-age poverty in Western Europe and North America
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
This paper addresses patterns, trends, and “pockets” of old-age poverty in Western Europe and North America since 2000, with a focus on five of the more financially resilient countries: Sweden, Germany, the Netherlands, Canada and the United States. Despite major public pension retrenchment initiatives in several of these countries, increases in both the breadth and depth of old-age poverty have been limited in most of these countries. Increases in old-age poverty that did occur were largely “collateral damage” from across-the board cutbacks in pension replacement rates and eligibility that were not adequately compensated for by increases in means-tested or minimum pensions. Poor retirees have only rarely been targeted directly for retrenchment in these countries. The most consistent pattern in the case studies is the role of policy drift--the production of different old-age poverty outcomes as the social and fiscal context within which government programs operate change, but policies do not. It is the limited positive power of poor retirees (their inability to get policy changes enacted that favor them) rather than their negative power (inability to block changes that hurt them) that has been more important as a driver of increased old-age poverty where it has occurred.
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.
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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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 it