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Record W3088029007 · doi:10.1111/spol.12655

Determinants of social assistance caseloads for employable single adults without dependants in Canada

2020· article· en· W3088029007 on OpenAlexaffabout
Nick Falvo, Ali Jadidzadeh

Bibliographic record

VenueSocial Policy and Administration · 2020
Typearticle
Languageen
FieldHealth Professions
TopicGlobal Health Care Issues
Canadian institutionsUniversity of CalgaryCarleton University
Fundersnot available
KeywordsTreasurySocial assistanceGovernment (linguistics)JurisdictionSocial securityBusinessValue (mathematics)Demographic economicsEconomic growthEconomicsPolitical scienceLabour economicsMarket economyLaw

Abstract

fetched live from OpenAlex

Abstract Government officials like the idea of just a small number of households in their respective jurisdiction receiving social assistance. A large number is seen as costly to the public treasury, and declining caseloads are generally viewed as a mark of success for both the economy and the government of the day. But what factors account for the size of a Canadian province's social assistance caseload? This article aims to shed light on this question, with a focus on single adults without dependants (and without serious disabilities) during the 1989–2017 period. One important finding is that when the value of social assistance benefit levels for this group increases by 1% in a province, the social assistance caseload for this demographic rises by 0.457%. Put differently, there is indeed an important behavior response associated with higher benefit levels. In response, we propose that provincial officials budget for higher take up levels when they increase benefit levels for this household group.

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)0.004
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 score0.020
Threshold uncertainty score0.144

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.004
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0020.002
Science and technology studies0.0030.001
Scholarly communication0.0010.000
Open science0.0010.001
Research integrity0.0000.001
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 head0.076
GPT teacher head0.441
Teacher spread0.365 · 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

Citations5
Published2020
Admission routes2
Has abstractyes

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