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Record W1498040833 · doi:10.56105/cjsae.v24i1.125

Marginalized Non-Traditional Adult Learners: Beyond Economics

2011· article· en· W1498040833 on OpenAlexafffundvenueabout
Tara Hyland‐Russell, Janet Groen

Bibliographic record

VenueCanadian Journal for the Study of Adult Education · 2011
Typearticle
Languageen
FieldSocial Sciences
TopicEducation Systems and Policy
Canadian institutionsUniversity of Calgary
FundersCanadian Council on Learning
KeywordsSociologyMathematics educationPsychologyNeoclassical economicsEconomicsPositive economics

Abstract

fetched live from OpenAlex

Changing demographics and patterns of higher education participation in many countries, including Canada, have prompted a growing interest in improving access for non-traditional adult learners. This paper focuses on the results of a research study funded by the Canadian Council on Learning that profiles 71 learners in three Canadian university-level Radical Humanities programs designed for low-income people. Four thematic areas explore the barriers and supports that impact students’ ability to access post-secondary education: (1) barriers to further education, (2) concept of self as learner, (3) learning space, and (4) role of the humanities. This paper argues that while poverty limits educational participation, a greater challenge is posed for marginalized non-traditional adult learners by complex relationships among economic and non-material barriers that limit their agency. Effectively increasing post-secondary participation rates for marginalized non-traditional adult learners cannot be achieved by addressing economic issues alone but by addressing the structural nature and impact of inter-related economic and non-material barriers.

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)0.001
metaresearch head score (Gemma)0.002
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 score0.825
Threshold uncertainty score0.349

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0030.003
Scholarly communication0.0030.002
Open science0.0010.003
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0030.000

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 head0.073
GPT teacher head0.326
Teacher spread0.253 · 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

Citations22
Published2011
Admission routes4
Has abstractyes

Explore more

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