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Policy Making for the Long Term in Advanced Democracies

2016· article· en· W2376865079 on OpenAlexaff
Alan M. Jacobs

Bibliographic record

VenueAnnual Review of Political Science · 2016
Typearticle
Languageen
FieldSocial Sciences
TopicSocial Policy and Reform Studies
Canadian institutionsUniversity of British Columbia
Fundersnot available
KeywordsEconomicsPoliticsPublic economicsWelfareSocial policyDemocracyPublic policySustainabilityConsumption (sociology)Political economyPolitical scienceMarket economySociologyEconomic growth

Abstract

fetched live from OpenAlex

A range of policy problems—from climate change to pension sustainability to skill shortages—confront governments with intertemporal dilemmas: trade-offs between maximizing social welfare in the present and taking care of the future. There is, moreover, substantial variation in the degree to which democratic governments are willing to invest in long-term social goods. Surprisingly, the literature on the politics of public policy has paid little explicit attention to timing as a dimension of policy choice, focusing almost exclusively on matters of cross-sectional distribution. This article develops a framework for explaining intertemporal policy choices in democracies by adapting findings from the literatures on distributive politics, political economy, and political behavior. The article makes a case for analyzing the politics of the long term as a struggle over how welfare should be allocated across groups and over how policy effects should be distributed through time.

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)0.009
metaresearch head score (Gemma)0.011
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: Theoretical or conceptual
GenreCandidate signal: Review · Consensus signal: none
Teacher disagreement score0.010
Threshold uncertainty score0.053

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0090.011
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.001
Science and technology studies0.0070.009
Scholarly communication0.0100.006
Open science0.0010.006
Research integrity0.0040.004
Insufficient payload (model declined to judge)0.0040.001

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 head0.033
GPT teacher head0.447
Teacher spread0.413 · 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 designTheoretical or conceptual
Domainnot available
GenreReview

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

Citations209
Published2016
Admission routes1
Has abstractyes

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