The Social Policies Presidents Make: Pre-Emptive Leadership under Nixon and Clinton
Bibliographic record
Abstract
Grounded in Stephen Skowronek's typology of presidential leadership, this paper furthers our understanding of ‘pre-emptive leadership’ through a comparative analysis of the welfare and Social Security reforms pursued by US presidents Richard Nixon and Bill Clinton. Although not identical, their experience in these areas provides valuable insight into the difficulty of wielding power in an inhospitable political environment. The paper starts with a brief presentation of Skowronek's typology before discussing the electoral strategies employed by both presidents as they attempted to frame political identities that would allow them to compete successfully in unfavourable ideological and political circumstances. The paper then specifically focuses on the politics of welfare and Social Security reform as the two presidents used these issues as part of their efforts to craft distinctive political images and attract wider electoral support. This comparative analysis reinforces the concept of ‘pre-emption’ as a valuable tool in understanding presidential behaviour. However, it also underlines the limits of pre-emptive leadership. Pre-emptive strategies can be effective at election time, but they are less likely to succeed in the legislative arena. This reality complicates the presidential search for genuine policy legacies.
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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 | 0.006 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.010 | 0.015 |
| Scholarly communication | 0.007 | 0.004 |
| Open science | 0.000 | 0.004 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 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 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".