Ideas and Institutional Change in Social Security: Conversion, Layering, and Policy Drift<sup>*</sup>
Bibliographic record
Abstract
Objectives.In recent years, social scientists such as Kathleen Thelen and Jacob Hacker have introduced new concepts to assist in the understanding of institutional change. Fostering some of these concepts, this article proceeds to augment the theoretical debate on institutional change in social science and policy research. A discussion of Social Security development in the United States advances the article's main objective: to uncover the relationship between ideational processes and policy development. Methods.Qualitative and historical analysis is offered to examine three major policy episodes: the enactment of the 1939 amendments, the first mandate of the Nixon Administration (1969–1972), and the push for Social Security privatization that emerged in the 1990s. Results.First, the analysis suggests that, through the process of institutional conversion, the 1939 amendments and the Nixon‐era reforms altered the nature of Social Security. Second, the discussion on Social Security privatization stresses the impact of layering and policy drift on public and private pensions. Conclusions.The concepts of conversion, layering, and policy drift receive further empirical support through the presented analysis. Moreover, this article suggests that, for a full understanding of institutional change, a systematic analysis of ideational processes is necessary.
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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 | 0.014 | 0.018 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.005 | 0.050 |
| Scholarly communication | 0.011 | 0.015 |
| Open science | 0.001 | 0.007 |
| Research integrity | 0.002 | 0.004 |
| 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".