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Record W2326750051 · doi:10.32920/23739531.v1

The Precarity Penalty: How Insecure Employment Disadvantages Workers and Their Families

2023· article· en· W2326750051 on OpenAlexafffundabout
Wayne Lewchuk, Michelynn Laflèche, Stephanie Procyk, Charlene Cook, Diane Dyson, Luin Goldring, Karen Lior, Alan Meisner, John Shields, Anthony Tambureno, Peter Viducis

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldHealth Professions
TopicEmployment and Welfare Studies
Canadian institutionsToronto Metropolitan UniversityMcMaster UniversityCollège LaflècheYork University
FundersSocial Sciences and Humanities Research Council of CanadaMcMaster University
KeywordsPrecarityEarningsPovertyDemographic economicsDistribution (mathematics)Labour economicsSociologyEconomic growthEconomicsGender studies

Abstract

fetched live from OpenAlex

<p>This paper examines the social and economic effects of precarious employment in the Greater Toronto-Hamilton area. The analysis is based on data from two surveys conducted in 2011 and in 2014 by the Poverty and Employment Precarity in Southern Ontario (PEPSO) research group. The survey findings paint a picture of how low earnings and economic uncertainty translate into delayed formation of relationships, lower marriage rates for workers under the age of 35, and fewer households with children. They also suggest that workers in precarious employment are more likely to experience social isolation. These findings suggest that the Precarity Penalty is not limited to economic outcomes from employment but also includes disadvantages in establishing healthy households and being engaged in one's community. Workers in secure employment enjoy better economic outcomes from employment that provide the basis for better household wellbeing and increased social integration. While much has been made in recent years of the unequal distribution of income, the PEPSO study also points to the unequal distribution of many of the non-financial aspects of life that people value including companionship, having a family and having friends.</p>

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 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)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesScience and technology studies
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.229
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0030.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
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 head0.055
GPT teacher head0.385
Teacher spread0.330 · 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 teacher head, not a consensus.

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

Citations34
Published2023
Admission routes3
Has abstractyes

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