Bibliographic record
Abstract
Abstract The latest wave of reforms of mandatory pension systems has included shifts away from the pay-as-you-go financing method towards partial or full funding. This is not the first time that funding has been attempted. Many pension systems started in the first quarter of this century were partially or fully funded. The provident funds created in ex-British colonies in the 1950s and 1960s also attempted funding. One new aspect of the most recent wave of reforms is that they have privatized pension provision. Examples are found in Chile (1981), Switzerland (1985), the United Kingdom (1988), Australia (1986 and 1992), Peru (1993), and Argentina (1994). In this chapter I link privatization to the political economy of pension systems. Pension policy is a creation of the state, not a market phenomenon. In addition, I argue that political analysis is more important in pension economics than in other areas of economic policy. The reason is that pension institutions must function suitably over a time horizon that spans six or more decades, a period long enough for the state itself to experience profound changes. Therefore, the optimal pension policy appears to be a function, first and foremost, of the stability of a country’s political regime.
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 | 0.001 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.004 | 0.001 |
| Open science | 0.000 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.013 | 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".