Bibliographic record
Abstract
Margaret Thatcher came to power in 1979 determined to control the growth of public expenditure, cut government deficits, and scale back the role of the state. As soaring unemployment placed upward pressure on social spending, however, the size of government increased rapidly during her first administration. When Nigel Lawson took office as Chancellor of the Exchequer in 1983, he sought both to reverse this disappointing trend in the near term and to hold down the long-term trajectory of government spending. While immediate cuts to programs such as defense and education helped the Chancellor pursue near-term fiscal discipline, pension reform would play a central role in his longer-term strategy. Old-age pensions were, on the one hand, a spending category in which quick savings were hard to achieve because current beneficiaries had come to depend on past benefit promises. At the same time, it was an area of expenditure that was scheduled to grow automatically over the next several decades as the ranks of retirees swelled. Accounts of the British pension reforms of the 1980s – especially those that set it in cross-national perspective – typically emphasize their relative radicalism (Huber and Stephens 2001a; Bonoli 2000). Thatcher's success in dismantling Britain's public retirement programs is usually considered dramatic by comparison, for instance, with Ronald Reagan's modest cutbacks to Social Security (Pierson 1994). This conventional view of the British reform outcome, however, occludes two important features of the case.
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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 | 0.003 |
| 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 | 0.003 |
| Scholarly communication | 0.007 | 0.007 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.028 | 0.009 |
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".