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Record W4405452843 · doi:10.1002/pop4.419

The Effect of Loosening Means‐Testing Requirements on Income Assistance Flows in British Columbia Canada

2024· article· en· W4405452843 on OpenAlexaffabout
Gillian Petit, Lindsay M. Tedds

Bibliographic record

VenuePoverty & Public Policy · 2024
Typearticle
Languageen
FieldEconomics, Econometrics and Finance
TopicFiscal Policy and Economic Growth
Canadian institutionsUniversity of Calgary
Fundersnot available
KeywordsEarningsArgument (complex analysis)EconomicsTest (biology)Asset (computer security)Labour economicsDemographic economicsBusinessActuarial sciencePublic economicsFinanceMedicineComputer science

Abstract

fetched live from OpenAlex

ABSTRACT In Canada, eligibility for provincial income assistance requires applicants and recipients to pass a means test—they must have income and assets below a specified level. The “good policy” argument argues that strict means tests ensure those with sufficient income or assets cannot access nor continue on income assistance, keeping caseloads low. The “bad policy” argument argues that strict means tests reduce the ability of income assistance recipients from becoming self‐sufficient. Exploiting an increase in asset thresholds and the introduction of an earnings exemption in British Columbia, we test these hypotheses using recipient‐level data. Our findings suggest that loosening means‐testing requirements is unlikely to result in an influx in new income assistance recipients but that it could encourage re‐entry; however, it could also help recipients exit income assistance permanently.

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)0.026
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 score0.097
Threshold uncertainty score0.706

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0040.026
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0020.003
Science and technology studies0.0070.003
Scholarly communication0.0040.001
Open science0.0030.003
Research integrity0.0010.003
Insufficient payload (model declined to judge)0.0070.001

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 head0.024
GPT teacher head0.224
Teacher spread0.199 · 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
Published2024
Admission routes2
Has abstractyes

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