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Record W4390012707 · doi:10.1177/15413446231222204

Measuring and Validating a Transformation Learning Survey Through Social Work Education Research

2023· article· en· W4390012707 on OpenAlex
Ana Isabel Corchado Castillo, Michael Wallengren-Lynch, Beth Archer‐Kuhn, Tara Earls Larrison

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueJournal of Transformative Education · 2023
Typearticle
Languageen
FieldSocial Sciences
TopicAdult and Continuing Education Topics
Canadian institutionsUniversity of Calgary
Fundersnot available
KeywordsTransformative learningJournaling file systemPedagogyQualitative propertyHigher educationWork (physics)PsychologySociologyMathematics educationComputer scienceEngineeringPolitical science

Abstract

fetched live from OpenAlex

This paper presents a reliable tool for measuring transformative learning in undergraduate social work education, the Social Work Transformation Survey (SWTS). The SWTS was developed from a qualitative theoretical model and translated into quantitative scales. The study collected data from 248 undergraduate students from eight countries who participated in a transnational project using creative journaling to facilitate transformative learning. Structural equation modelling was used to validate the internal structure of the SWTS. We then confirmed the measures’ reliability, and subsequently the effectiveness of creative journaling practices as a pedagogy for facilitating transformative learning in social work students. This paper highlights the potential of combining qualitative and quantitative research approaches to develop educational evaluation tools for higher education settings and presents one specific measure for transformative learning.

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.

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)0.007
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesScience and technology studies
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.263
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0070.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.002
Science and technology studies0.0020.000
Scholarly communication0.0000.002
Open science0.0000.000
Research integrity0.0000.001
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 head0.201
GPT teacher head0.452
Teacher spread0.251 · 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