The Politics of When: Redistribution, Investment and Policy Making for the Long Term
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
Why do some elected governments impose short-term costs to invest in solving long-term social problems while others delay or merely redistribute the pain? This article addresses that question by examining the politics of pension reform in Britain and the United States. It first reframes the conventional view of the outcomes – centred on cross-sectional distribution – demonstrating that the politicians who enacted the least radical redistribution enacted the most dramatic intertemporal tradeoffs. To explain this pattern, the article develops and tests a theory of policy choice in which organized interests struggle for long-term advantage under institutional constraints. The argument points to major analytical advantages to studying governments' policy choices in intertemporal terms, for both the identification of comparative puzzles and their explanation.
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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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.005 | 0.010 |
| 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