Bibliographic record
Abstract
Abstract Because they are funded on a pay-as-you-go basis, universal state pension schemes are long-term intergenerational contracts. In them, one working generation (G2) contracts with retirees (G1) to fund their retirement. Unlike in a standard contract, G1 does not offer anything to G2. Rather, G3 (G2’s children and grandchildren) will be expected to fund G2’s retirement in turn. In this way, G1 and G2 have bound G3 into a contract without their tacit or express consent (because they do not exist to give it at the time of the contract). In this chapter the author interrogates the foundational question of whether an intergenerational contract of this nature is just. The author anticipates that a model of hypothetical consent will help make sense of the binding nature of such a contract. However, the author also argues that if hypothetical consent is relied upon to justify such contracts, it will place unexpected obligations on G2, including the obligation to reproduce or support high levels of immigration, and rights for G3, including the right to heavily tax G2 if they do not discharge the aforementioned duties.
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 | 0.002 | 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 | 0.002 | 0.005 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".