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Record W2948827318 · doi:10.3138/cpp.2019-012

Social Policy and Income Mobility: An Interprovincial Perspective

2019· article· en· W2948827318 on OpenAlexaffvenueabout
Guy Lacroix

Bibliographic record

VenueCanadian Public Policy · 2019
Typearticle
Languageen
FieldSocial Sciences
TopicHealth disparities and outcomes
Canadian institutionsUniversité LavalHEC Montréal
Fundersnot available
KeywordsPovertyDisadvantagedSocial mobilityGovernment (linguistics)EconomicsDemographic economicsEconomic mobilitySocial policyDevelopment economicsPerspective (graphical)Public policyEconomic growthPolitical science

Abstract

fetched live from OpenAlex

In this article, I analyse intragenerational income mobility and the long-term dynamics of poverty. The proposed analysis is based on the Longitudinal and International Study of Adults and covers the period from 1983 to 2011. This period encompasses major social reforms that were implemented in Quebec and Ontario in the 1990s. In Quebec, several innovative policies were implemented with the aim of promoting the integration of economically disadvantaged people into the labour market. In Ontario, the government instead relied on coercive policies to achieve the same ends. The analysis proposes to indirectly investigate the effects of these reforms on income mobility and poverty dynamics. To do this, I present a Quebec–Ontario comparative study using several distinct cohorts. Although many studies have found that income disparities and relative poverty are lower in Quebec, the data do not allow me to conclude that the policies that were implemented in Quebec have had a significant effect on social mobility or on the dynamics of poverty.

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: Empirical
Teacher disagreement score0.120
Threshold uncertainty score0.241

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0020.003
Science and technology studies0.0040.003
Scholarly communication0.0030.002
Open science0.0010.003
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0120.000

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 head0.023
GPT teacher head0.360
Teacher spread0.337 · 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

Citations6
Published2019
Admission routes3
Has abstractyes

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