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Record W2071273054 · doi:10.1177/0143831x06065960

Why ‘Good’ Jobs Lead to Social Exclusion

2006· article· en· W2071273054 on OpenAlexaffabout
Charlotte Yates, Belinda Leach

Bibliographic record

VenueEconomic and Industrial Democracy · 2006
Typearticle
Languageen
FieldHealth Professions
TopicEmployment and Welfare Studies
Canadian institutionsUniversity of GuelphMcMaster University
Fundersnot available
KeywordsSocial exclusionRestructuringInclusion (mineral)Work (physics)SociologyFeelingPublic relationsSocial psychologyLabour economicsPolitical sciencePsychologyEconomic growthGender studiesEconomicsLawEngineering

Abstract

fetched live from OpenAlex

This article challenges analyses that connect engagement in paid work with social inclusion. The article critiques much of the existing literature for its simplistic connectivity between paid work and social inclusion, arguing instead for an approach that recognizes the reciprocal and interactive relations between paid work, structural labour market factors and the everyday lives of working people. Drawing upon a selection of interviews of working people in Canada, the article examines how workers experience control, respect and trust and insecurity in the labour process, their work-life balance and the labour market. The article concludes that restructuring of work and the labour market have encouraged feelings and life practices that result in isolation, anger and a declining capacity of working people to look after their families and participate in their communities. In short, engagement in paid work, even so-called ‘good’ work, is leading to social exclusion rather than inclusion.

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)0.007
metaresearch head score (Gemma)0.013
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.017
Threshold uncertainty score0.035

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0070.013
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0020.001
Science and technology studies0.0170.038
Scholarly communication0.0090.004
Open science0.0010.011
Research integrity0.0030.003
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 head0.077
GPT teacher head0.357
Teacher spread0.281 · 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 designQualitative
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

Citations13
Published2006
Admission routes2
Has abstractyes

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