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Record W4409657838 · doi:10.31235/osf.io/fa5j4_v1

Are large age groups hijacking the welfare state? The effects of relative cohort size on five areas of social spending

2017· preprint· en· W4409657838 on OpenAlexaff
Charles Plante, Jason O. Jensen

Bibliographic record

Venuenot available
Typepreprint
Languageen
FieldHealth Professions
TopicGlobal Health Care Issues
Canadian institutionsMcGill University
Fundersnot available
KeywordsWelfareCohortWelfare stateSocial WelfareState (computer science)Demographic economicsPsychologyEconomicsPublic economicsBusinessDemographyPolitical scienceStatisticsComputer scienceSociologyMathematicsMarket economyPolitics

Abstract

fetched live from OpenAlex

Chronos politics refers to the political mobilization of age groups and/or cohorts. In the popular media, commentators accuse Baby Boomers of using their large relative cohort size to force welfare states to shortchange smaller generations. Ironically, Western society used to be afraid that their disproportionate size would cause the Baby Boomers to be shortchanged. Our paper uses population and expenditure data on 24 countries to explore how the relative size of age groups affects expenditures in five areas of social spending: active labour market policies (ALMP), family, health, old age, and unemployment. We find that contrary to popular perceptions, per capita spending tends to lag behind demographic changes, resulting in shortages and windfalls for larger and smaller generations, respectively.

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)0.004
metaresearch head score (Gemma)0.013
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.023
Threshold uncertainty score0.045

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0040.013
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0010.001
Science and technology studies0.0010.002
Scholarly communication0.0020.002
Open science0.0000.002
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0100.001

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 head0.047
GPT teacher head0.431
Teacher spread0.385 · 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 designObservational
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

Citations0
Published2017
Admission routes1
Has abstractyes

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