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Record W2113237848 · doi:10.1080/02601370.2015.1030349

Challenging change: transformative education for economically disadvantaged adult learners

2015· article· en· W2113237848 on OpenAlexaff
Tara Hyland‐Russell, Corinne Syrnyk

Bibliographic record

VenueInternational Journal of Lifelong Education · 2015
Typearticle
Languageen
FieldSocial Sciences
TopicAdult and Continuing Education Topics
Canadian institutionsSt. Mary's University
Fundersnot available
KeywordsTransformative learningDisadvantagedAgency (philosophy)Context (archaeology)Sense of agencyPedagogyAdult educationSociologyNarrativePsychologySocial psychologyPolitical scienceSocial science

Abstract

fetched live from OpenAlex

This paper focuses on the results of a mixed-methods study of 13 participants in a Radical Humanities programme designed as a transformative learning space for low-income adults who have experienced barriers to learning. Using demographic questionnaires, semi-structured narrative interviews and course evaluations this study examined participants' experiences in the programme and the impact on their learning, sense of agency and future ambitions. As the first phase of a longitudinal project on the well-being and agency of under-represented and marginalized learners, this preliminary study revealed five predominant themes emerging from learners' experiences: (1) self-reflective meaning-making processes; (2) interrelated personal and communal growth; (3) appreciation of diversity; (4) emerging sense of self-as-learner; (5) renewed aspirations. This paper argues that transformative learning for low-income adults is a complex and challenging process that entails participants' ongoing negotiations of self, learning, and purpose. Understanding and evaluating the effectiveness of transformative learning within a social-emancipatory humanities programme requires attention to the programme's social context and choices faced by participants. Programme participation fosters increased well-being, deeper relationships and hope for the future. However, these tangible benefits are tempered by constraints of individual and structural systems that, for some students, impose limitations on their ability to enact change in themselves and in their lives.

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)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.594
Threshold uncertainty score0.462

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.001
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 head0.055
GPT teacher head0.405
Teacher spread0.350 · 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 designNot applicable
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
Published2015
Admission routes1
Has abstractyes

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