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Record W2331478975 · doi:10.1515/for-2015-0002

The Segmented Third Rail: The Politics of Social Security from Carter to Obama

2015· article· en· W2331478975 on OpenAlexaff
Daniel Béland

Bibliographic record

VenueThe Forum · 2015
Typearticle
Languageen
FieldSocial Sciences
TopicSocial Policy and Reform Studies
Canadian institutionsPublic Safety Canada
Fundersnot available
KeywordsPoliticsMainstreamSocial securitySocial policyVariety (cybernetics)Public administrationPolitical economyPolitical scienceLaw and economicsEconomicsSociologyLaw

Abstract

fetched live from OpenAlex

Abstract Social Security is widely known as “The Third Rail of American Politics” and the program has proved been remarkably stable and politically resilient, over time. While true, this basic story about the institutional stability of Social Security in the US should not hide the segmented, discontinuous nature of its politics over the last four decades. As suggested in this article, the changing nature of Social Security politics is reflected in the variety of the policy options debated, which range from incremental benefit cuts and revenue increases adopted during the Carter and the Reagan years to structural, path-shifting proposals associated with the idea of Social Security privatization, which only became a mainstream policy alternative during the Clinton years. The following analysis explores the debate about these diverse policy alternatives, if and when they entered the federal policy agenda during the periods under consideration.

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 imitation

Not 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.

metaresearch head score (Codex)0.005
metaresearch head score (Gemma)0.006
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.065
Threshold uncertainty score0.130

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0050.006
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0170.012
Scholarly communication0.0100.004
Open science0.0010.005
Research integrity0.0050.006
Insufficient payload (model declined to judge)0.0050.000

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.

Opus teacher head0.035
GPT teacher head0.333
Teacher spread0.299 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designQualitative
Domainnot available
GenreEmpirical

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".

Quick stats

Citations2
Published2015
Admission routes1
Has abstractyes

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