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Record W4415295334 · doi:10.69520/jipe.v7i1.271

Post-secondary Education in Crisis: The Decline of Social Mobility and the Future of Learning in Canada

2025· article· en· W4415295334 on OpenAlexaffabout
Mira Kapetanovic

Bibliographic record

VenueJournal of innovation in polytechnic education. · 2025
Typearticle
Languageen
FieldSocial Sciences
TopicEducation Systems and Policy
Canadian institutionsHumber Polytechnic
Fundersnot available
KeywordsWorkforceVocational educationSocial mobilityLifelong learningGovernment (linguistics)CurriculumSocioeconomic statusPublic policy

Abstract

fetched live from OpenAlex

By 2040, Canada's post-secondary education (PSE) landscape is expected to have undergone a profound transformation, challenging its long-held status as a reliable pathway to upward social mobility. Historically, earning a college or university degree was viewed as a guaranteed route to stable employment, higher income, and improved social standing. However, predicted escalating tuition fees, soaring housing costs, extended program durations, and curricula misaligned with evolving job market demands will render PSE increasingly inaccessible to all but the wealthiest Canadians in a few short decades. This paper explores the socioeconomic consequences of these trends, including widening class divides, underemployment, skills mismatches, and the erosion of education's role as a public good. Drawing on government reports, labour market data, and emerging educational models, the analysis identifies sustainable alternatives such as modular micro-credentials, vocational and technical education, work-integrated learning, lifelong reskilling ecosystems, and community-based models. Ultimately, the study argues that for Canadian post-secondary institutions to remain relevant, they must embrace comprehensive reforms centred on affordability, flexibility, and more substantial alignment with contemporary workforce demands. Without such transformation, the risk deepens the polarization of opportunity, threatening both individual prospects and societal cohesion.

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)0.004
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation 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 score0.547
Threshold uncertainty score0.968

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0040.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.003
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 head0.008
GPT teacher head0.333
Teacher spread0.325 · 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

Citations0
Published2025
Admission routes2
Has abstractyes

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