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Record W2601387058 · doi:10.1177/1541344617699591

Shame and Transformation in the Theory and Practice of Adult Learning and Education

2017· article· en· W2601387058 on OpenAlexaff
Jude Walker

Bibliographic record

VenueJournal of Transformative Education · 2017
Typearticle
Languageen
FieldSocial Sciences
TopicAdult and Continuing Education Topics
Canadian institutionsUniversity of British Columbia
Fundersnot available
KeywordsShameTransformative learningPsychologyAdult educationConversationAdult LearningUnconscious mindSocial psychologyPedagogySociologyPsychoanalysisCommunication

Abstract

fetched live from OpenAlex

Shame both stymies and motivates learning; it prevents adults from participating in educational programs yet, with accompanied self-examination, it can be the catalyst for transformation. While fundamental for understanding adult learning, shame is (shamefully) inadequately theorized in the field of adult education: We don’t talk enough about shame. The purpose of this article is to spark a conversation about shame in adult learning and education and to inspire the transformative learning community to undertake further empirical and theoretical analyses of shame in adult learning. As Dirkx implored us over a decade ago, we need to stop shying away from emotions in adult learning and to begin to courageously engage that which lies in the unconscious but which drives our everyday actions and interactions. Shame is at the core of who we are—and who we can become—as adult learners and educators.

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)0.018
metaresearch head score (Gemma)0.017
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: Theoretical or conceptual
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.018
Threshold uncertainty score0.093

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0180.017
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0020.001
Science and technology studies0.0080.155
Scholarly communication0.0100.014
Open science0.0010.009
Research integrity0.0050.007
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 head0.013
GPT teacher head0.373
Teacher spread0.360 · 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 designTheoretical or conceptual
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

Citations50
Published2017
Admission routes1
Has abstractyes

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