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Record W1791196730 · doi:10.1111/1468-2427.12088

Generational Dimensions of Neoliberal and Post‐Fordist Restructuring: The Changing Characteristics of Young Adults and Growing Income Inequality in Montreal and Vancouver

2013· article· en· W1791196730 on OpenAlexaffabout
Markus Moos

Bibliographic record

VenueInternational Journal of Urban and Regional Research · 2013
Typearticle
Languageen
FieldEconomics, Econometrics and Finance
TopicHousing, Finance, and Neoliberalism
Canadian institutionsUniversity of Waterloo
Fundersnot available
KeywordsRestructuringInequalityEducational attainmentDemographic economicsContext (archaeology)EarningsFordismEconomicsEconomic inequalityLabour economicsEconomic restructuringIncome distributionSociologyEconomic growthGeographyEconomy

Abstract

fetched live from OpenAlex

Abstract There is growing concern over income inequality and its generational dimensions. Post‐ F ordist and neoliberal restructuring have reshaped urban labour markets, resulting in growing inequalities that disproportionately afflict younger workers. This article empirically analyses the transition as experienced in M ontreal and V ancouver, two C anadian cities that have undergone restructuring in different ways. The study of young adults' changing incomes reveals growing intra‐ and inter‐cohort inequality, and an increasing intergenerational income gap in both cities. Income inequality is greater in V ancouver, with its more pronounced post‐ F ordist labour force composition and neoliberalized governance context. Known factors such as occupation and gender affect the earnings structure, but educational attainment has increased the most in terms of its effect on incomes. Inequalities among young adults are expected to magnify in the future due to unevenness in educational attainment. Urban research ought to pay close attention to the role of education in structuring inequalities, and the ways the impact of restructuring is unevenly distributed across generations.

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 categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.109
Threshold uncertainty score0.312

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 studies0.0000.000
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 head0.035
GPT teacher head0.272
Teacher spread0.237 · 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

Citations26
Published2013
Admission routes2
Has abstractyes

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