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Record W2591275415 · doi:10.1177/1541344617692772

Creativity as a Driver for Transformative Learning

2017· article· en· W2591275415 on OpenAlexaffabout
Meagan Troop

Bibliographic record

VenueJournal of Transformative Education · 2017
Typearticle
Languageen
FieldSocial Sciences
TopicAdult and Continuing Education Topics
Canadian institutionsUniversity of Waterloo
Fundersnot available
KeywordsTransformative learningAutoethnographyCreativityContext (archaeology)PedagogyPsychologyExploratory researchNarrativeFocus groupSociologyMathematics educationSocial psychologySocial science

Abstract

fetched live from OpenAlex

This exploratory study identifies aspects of pedagogical design and teaching practice that enabled creative capacities through the lens of the researcher’s lived experience. A guiding research question in this investigation follows: (a) What is the nature of the relationship between creative activity and transformative learning and (b) In what ways are they connected through the lived experience? To conduct this exploratory study, I adopted a dual role as researcher and student in the context of a PhD-level education course at a university in Ontario, Canada. A methodological approach that drew on elements of narrative, self-study, and autoethnography was applied. Data sources include (a) field notes, (b) teaching and learning materials, (c) an individual interview with the instructor, and (d) a focus group with the other four female students in the course. Participants of the study reported that their traditional perspectives of academia were shaped and changed in the context of the creative activities and interactions. Findings reveal how creative acts served as a catalyst for transforming the ways in which the instructor and the students in the course experienced knowledge making.

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)0.009
metaresearch head score (Gemma)0.025
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: Empirical
Teacher disagreement score0.013
Threshold uncertainty score0.048

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0090.025
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0020.001
Science and technology studies0.0050.016
Scholarly communication0.0130.008
Open science0.0010.008
Research integrity0.0020.003
Insufficient payload (model declined to judge)0.0060.001

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 head0.026
GPT teacher head0.406
Teacher spread0.380 · 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

Citations10
Published2017
Admission routes2
Has abstractyes

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