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Social welfare matters: A realist review of when, how, and why unemployment insurance impacts poverty and health

2015· review· en· W2008044738 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueSocial Science & Medicine · 2015
Typereview
Languageen
FieldHealth Professions
TopicEmployment and Welfare Studies
Canadian institutionsWilfrid Laurier UniversityUniversity of Toronto
FundersMedical Research Council
KeywordsUnemploymentGenerosityPovertyEconomicsRecessionWelfareWelfare reformConsumption (sociology)Welfare stateDistressEarningsCulture of povertyLabour economicsDemographic economicsBasic needsEconomic growthSociologyPolitical sciencePsychology

Abstract

fetched live from OpenAlex

The recent global recession and concurrent rise in job loss makes unemployment insurance (UI) increasingly important to smooth patterns of consumption and keep households from experiencing extreme material poverty. In this paper, we undertake a realist review to produce a critical understanding of how and why UI policies impact on poverty and health in different welfare state contexts between 2000 and 2013. We relied on literature and expert interviews to generate an initial theory and set of propositions about how UI might alleviate poverty and mental distress. We then systematically located and synthesized peer-review studies to glean supportive or contradictory evidence for our initial propositions. Poverty and psychological distress, among unemployed and even the employed, are impacted by generosity of UI in terms of eligibility, duration and wage replacement levels. Though unemployment benefits are not intended to compensate fully for a loss of earnings, generous UI programs can moderate harmful consequences of unemployment.

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.

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.006
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Science and technology studies
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.317
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0060.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0030.000
Bibliometrics0.0000.001
Science and technology studies0.0030.002
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0000.000

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 head0.184
GPT teacher head0.499
Teacher spread0.314 · 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