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Record W2310758420 · doi:10.1177/0741713616640881

Undressing Transformative Learning

2016· article· en· W2310758420 on OpenAlex

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

VenueAdult Education Quarterly · 2016
Typearticle
Languageen
FieldSocial Sciences
TopicAdult and Continuing Education Topics
Canadian institutionsUniversity of Manitoba
Fundersnot available
KeywordsTransformative learningAction (physics)PsychologyClothingAction learningQualitative propertyCognitive psychologySocial psychologyCooperative learningDevelopmental psychologyPedagogyTeaching methodComputer science

Abstract

fetched live from OpenAlex

Clothing is an integral part of our lives, yet modes of producing, using, and disposing of apparel have significant impacts on the environment. Our research explored the role transformative learning plays in the transition to more sustainable thinking and actions about clothing to illuminate instrumental learning processes and examine the relationship between instrumental and communicative learning. Using a qualitative case study approach, we gathered data on behaviors and attitudes ( n = 32), and examined in depth the learning participants underwent and the action they took ( n = 17). The data reveal that instrumental and communicative learning outcomes were plentiful, with participants discussing the array of skills, knowledge, and communicative insights they learned. Results indicate that instrumental learning makes action possible by allowing individuals to identify problems and solutions and to develop plans of action. Results also reveal the important interaction among instrumental and communicative learning as an individual seeks to understand an occurrence.

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)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Other design · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.757
Threshold uncertainty score0.410

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
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 head0.007
GPT teacher head0.306
Teacher spread0.299 · 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