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Record W3003382362 · doi:10.5539/ijef.v12n2p45

War and Pensions: The Effects of War on Social Security and Pensions Around the World

2020· article· en· W3003382362 on OpenAlexvenueno aff
John A. Turner, David Rajnes, Gerard Hughes, Michelle Maher

Bibliographic record

VenueInternational Journal of Economics and Finance · 2020
Typearticle
Languageen
FieldHealth Professions
TopicEmployment and Welfare Studies
Canadian institutionsnot available
FundersIowa Pork Producers Association
KeywordsSocial securitySolidarityEconomicsEarningsPensionAdministration (probate law)Social insuranceWork (physics)World War IIPolitical scienceFinanceLawMarket economyPolitics

Abstract

fetched live from OpenAlex

War has affected the development of social security and employer-provided pensions. Roman soldiers received the first pensions. In most countries, military pensions preceded social security pensions, providing countries experience with the concept and administration of pensions. War or the threat of war affected the development of the two major branches of social insurance-based pensions—Bismarckian (earnings related pensions developed in Germany) and Beveridgian (pensions tied to years of work developed in the United Kingdom). War has affected the choice countries make between funded and unfunded or pay-as-you-go pensions. The money in funded social security pensions can be expropriated to finance wars. Periods of hyperinflation following wars have destroyed funded social security pensions and funded employer-provided pensions in some countries. A victor country can have a major effect on the pensions in a defeated country. Social security pensions can be used to encourage national solidarity during and after wars. In the twenty-first century, social security pensions have been the target of cyber warfare.

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)0.001
metaresearch head score (Gemma)0.002
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: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.011
Threshold uncertainty score0.036

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.002
Science and technology studies0.0020.002
Scholarly communication0.0020.003
Open science0.0000.002
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0110.001

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 head0.048
GPT teacher head0.349
Teacher spread0.301 · 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

Citations2
Published2020
Admission routes1
Has abstractyes

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