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Record W2288138008

Lived Experiences of Unemployed Women in Toronto and Halifax, Canada Who Were Previously Precariously Employed

2016· article· en· W2288138008 on OpenAlexvenueaboutno aff
Leslie Nichols

Bibliographic record

VenueAlternate routes · 2016
Typearticle
Languageen
FieldHealth Professions
TopicEmployment and Welfare Studies
Canadian institutionsnot available
Fundersnot available
KeywordsPrecarityWorkfareUnemploymentPrecarious workWelfare stateWelfareNeoliberalism (international relations)Labour economicsState (computer science)RetrainingSociologyPolitical scienceDemographic economicsEconomicsGender studiesEconomic growthPolitical economyWork (physics)
DOInot available

Abstract

fetched live from OpenAlex

Over the past few years, there has been an increase in the number of workers in Canada who are not in standard employment relations but are instead in contract, part-time, or otherwise precarious employment. At the same time, the neoliberal policy paradigm has replaced the belief that we should support workers through full-time stable employment with an idea that labour can be utilized whenever and however required, as dictated by the economy’s needs. The detrimental effects of neoliberal market policies are well known. Further exploration is needed on the differential impacts of these policies on women with intersectional identities, particularly in an era of increasing employment precarity. Based on a qualitative study of unemployed women’s lived experiences in Toronto and Halifax, this article explores the issues surrounding unemployment, including financial impacts, job searching, retraining, and health impacts of unemployment and employment precarity. The results were analyzed using intersectional and grounded theory. The study concludes with key results related to the impact of precarity in the labour market: Neoliberal erosion of the welfare state is manifested in a lack of supports for workers.

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)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation 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 score0.138
Threshold uncertainty score0.609

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
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 head0.024
GPT teacher head0.333
Teacher spread0.309 · 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.

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

Citations1
Published2016
Admission routes2
Has abstractyes

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